Systems and methods for geospatial value subject analysis and management

ABSTRACT

Location information may be gathered, managed, stored, and/or otherwise utilized to determine unique geo-referenced locations. The geo-referenced locations may be utilized to inform various processes and decisions such as insurance underwriting, risk assessment, pricing, and risk/loss control. Geo-referenced location information may also be utilized to allow for user-defined or customized value subject data gathering, analysis, and management.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims benefit and priority to, and is aContinuation of U.S. patent application Ser. No. 14/316,099 filed onJun. 26, 2014 and titled “SYSTEMS AND METHODS FOR GEOSPATIAL VALUESUBJECT ANALYSIS AND MANAGEMENT”, and issued as U.S. Pat. No. ______ on______, 2020, which itself claims benefit and priority to and is aContinuation-In-Part (CIP) of U.S. patent application Ser. No.13/836,429 filed on Mar. 15, 2013 and titled “SYSTEMS AND METHODS FORCERTIFIED LOCATION DATA COLLECTION, MANAGEMENT, AND UTILIZATION”, whichissued as U.S. Pat. No. 9,953,369 on Apr. 24, 2018 and which itselfclaims benefit and priority to U.S. Provisional Patent Application No.61/616,629 filed on Mar. 28, 2012 and titled “SYSTEMS AND METHODS FORCERTIFIED LOCATION DATA COLLECTION, MANAGEMENT, AND UTILIZATION”, theentirety of each such application hereby being incorporated by referenceherein.

BACKGROUND

Location data, such as address data associated with a customer, is oftenimprecise, incomplete, provided in non-standardized formats, and/oroverlaps or conflicts with other location information. Such deficienciesin location information cause various location-based attributes toremain hidden, which may for example, hinder the effectiveness ofvarious business decisions. Insurance and/or other underwriting orrisk-based product quotations or sales may, for example, be adverselyaffected by utilization of currently-available address or location data.Business decisions based on location information also typically sufferfrom decreased accuracy, reliability, and/or usability in standardsystems.

BRIEF DESCRIPTION OF THE DRAWINGS

An understanding of embodiments described herein and many of theattendant advantages thereof may be readily obtained by reference to thefollowing detailed description when considered with the accompanyingdrawings, wherein:

FIG. 1 is a block diagram of a system according to some embodiments;

FIG. 2A is a top view of an example location according to someembodiments;

FIG. 2B is a perspective view of the example location of FIG. 2A,according to some embodiments;

FIG. 2C is a top view of a portion of the example location of FIG. 2A,according to some embodiments;

FIG. 2D is a perspective view of the example location of FIG. 2A,according to some embodiments;

FIG. 3A is a block diagram of an example data storage structure of asystem according to some embodiments;

FIG. 3B is a block diagram of an example location data set mapped to anexample data storage structure of a system according to someembodiments;

FIG. 3C is a block diagram of an example data storage structure of asystem according to some embodiments;

FIG. 3D is a block diagram of an example rule set of a system accordingto some embodiments;

FIG. 4 is a flow diagram of a method according to some embodiments;

FIG. 5A and FIG. 5B are diagrams of an example data storage structureaccording to some embodiments;

FIG. 6 is a block diagram of a system according to some embodiments;

FIG. 7 is a flow diagram of a method according to some embodiments;

FIG. 8 is a flow diagram of a method according to some embodiments;

FIG. 9 is a diagram of a system according to some embodiments;

FIG. 10 is a flow diagram of a method according to some embodiments;

FIG. 11 is a flow diagram of a method according to some embodiments;

FIG. 12 is a flow diagram of a method according to some embodiments;

FIG. 13 is a diagram of an exemplary risk matrix according to someembodiments;

FIG. 14 is a flow diagram of a method according to some embodiments;

FIG. 15 is a block diagram of an apparatus according to someembodiments; and

FIG. 16A, FIG. 16B, FIG. 16C, FIG. 16D, and FIG. 16E are perspectivediagrams of exemplary data storage devices according to someembodiments.

DETAILED DESCRIPTION

Embodiments described herein are descriptive of systems, apparatus,methods, and articles of manufacture for geospatial value subjectanalysis and management as well as certified location data collection,management, and utilization. In some embodiments, for example, locationdata may be collected, retrieved, aggregated, sorted, filtered,standardized, and/or otherwise processed to define relationships betweenvarious location data elements or types. According to some embodiments,location data may be stored, processed, and/or presented in a mannerthat facilitates enhanced value subject definition and/or analysis.Users of location data provided in accordance with embodiments herein,for example, may be provided with a specially-programmed interface thatpermits free-form value subject definition and/or access to desiredvalue subject data in the absence of provided address information (e.g.,utilizing a Graphical User Interface (GUI)-based value subject selectiontool).

As utilized herein, the term “value subject” generally refers to aphysical area and/or object that is defined and analyzed with respect torisk. Value objects may comprise, for example (but are not limited to),parcels of land, buildings, other structures (e.g., cellular telephonetransmission towers, water tanks/towers, and/or smoke stacks), roadsegments, travel routes, etc. According to some embodiments, a valuesubject may comprise a set, group, and/or collection of items,locations, and/or objects that are insured based on an assumption thatsuch items, locations, and/or objects are likely subject to the sameperil, likelihood of peril, and/or magnitude of peril. According to someembodiments, a value subject may comprise a series or group of points,lines, or polygons, such as in association with or defining particularroad or surface segments, such as those described in co-pending U.S.patent application Ser. No. 13/723,685 filed on Dec. 21, 2012 and titled“SYSTEMS AND METHODS FOR SURFACE SEGMENT DATA”, the surface segmentconcepts and descriptions of which are hereby incorporated by referenceherein. According to some embodiments, a value subject may comprise aseries or group of points, lines, or polygons, such as in associationwith or defining particular risk zones, such as those described in U.S.Pat. No. 8,682,699 issued on Mar. 25, 2014 and titled “SYSTEMS ANDMETHODS FOR CUSTOMER-RELATED RISK ZONES”, the risk zone concepts anddescriptions of which are hereby incorporated by reference herein. Insome embodiments, a particular and/or unique value subject may beidentified and/or defined by one or more certified locations.

As utilized herein, the term “certified location” generally refers to alocation that is uniquely identifiable. In some embodiments, forexample, a certified location comprises a specific geo-referenced pointor set of points defining a discrete area and/or object (e.g., auniquely identifiable place on the earth). According to someembodiments, the level of granularity of data descriptive of a certifiedlocation may change over time. A single building on a parcel of land maycomprise a certified location when owned and/or operated by a singleentity, for example, but in the case that the building is sub-dividedand/or becomes associated with more than the single entity (e.g.,acquires multiple tenants), more detailed data may be storeddistinguishing the different portions of the building (e.g., uniquely).In some embodiments, a certified location comprises a point and/orpolygon representing a unique location and identified by a uniqueidentifier (e.g., a certified location certificate number).

In accordance with some embodiments herein, uniqueness and/orgranularity of certified location data may be dependent upon an existingdata set. In the case of a database of customer data, for example, astreet address may be unique within the dataset, but may not be uniquewith respect to geo-spatial, geo-political, civil, municipal, and/orother considerations and/or data sets. More than one person or businessmay occupy the street address, for example, but only one of suchindividuals may be a customer for which data is currently stored. Insuch embodiments, the street address may comprise a certified locationuntil more detailed data and/or granularity is required to distinguishthe information descriptive of the location of the customer frominformation descriptive of a location of another (e.g., new and/orprospective) customer.

As utilized herein, the term “customer” may generally refer to any type,quantity, and or manner of entity with or for which location,underwriting product and/or policy, and/or underwriting product riskand/or premium information may be determined in accordance withembodiments described herein. A customer may comprise an individual,personal, and/or business insurance policy holder, for example, and/ormay comprise an individual, family, business, and/or other entity thatseeks to price and/or obtain an insurance and/or other underwritingproduct and/or policy as described herein. A customer may have anexisting business relationship with other entities described herein,such as an insurance company for example, or may not yet have such arelationship—i.e., a “customer” may comprise a “potential customer”(e.g., in general and/or with respect to a specific product offering).

As utilized herein, the term “polygon” may generally refer to any type,quantity, representation, and/or configuration of an area and/or volumethat is or becomes known or practicable. According to some embodiments,a polygon may identify and/or define a particular value subject. In someembodiments, polygons may comprise regular polygons, irregular polygons,and/or any other two or three-dimensional shape and/or orientation thatis or becomes known or practicable—e.g., circles, ellipses, cones,pyramids, cylinders, and/or combinations of various shapes. A landparcel, which is representative of one type of polygon/area for example,may be defined in accordance with some embodiments by one or moregeospatial points and/or coordinates. In some embodiments, a polygon maybe identified by a representative (e.g., best available) point and/orcoordinate. According to some embodiments, a polygon may be identifiedby a plurality of points and/or coordinates such as may berepresentative of (and/or define) one or more vertices, midpoints,endpoints, intersections, and/or other geometric features of a polygon.Polygons may comprise a variety of shapes and may be defined by variousentities. Polygons (and/or value subjects) may be defined as (or by) taxparcels and corresponding geospatial survey points, for example, or maybe manually drawn on a map by an insurance underwriter, analyst, agent,data customer, other user, etc. (e.g., to define a polygon as an area ofinterest to the insurance business, which may or may not correspond inwhole or in part to one or more tax or zoning parcel boundaries). Insome embodiments, a polygon may define a certified location representingan area and/or object for which an insurance and/or underwriting productis written (e.g., a value subject such as a particular building, landparcel, apartment, and/or structure—e.g., a cell tower). According tosome embodiments, a polygon may define a certified locationrepresentative of an area and/or object associated with (but differentfrom) an area and/or object for which an insurance and/or underwritingproduct is written (e.g., a property and/or structure adjacent to aninsured property and/or value subject).

As used herein, the term “network component” may refer to a user ornetwork device, or a component, piece, portion, or combination of useror network devices. Examples of network components may include a StaticRandom Access Memory (SRAM) device or module, a network processor, and anetwork communication path, connection, port, or cable.

In addition, some embodiments are associated with a “network” or a“communication network.” As used herein, the terms “network” and“communication network” may be used interchangeably and may refer to anyobject, entity, component, device, and/or any combination thereof thatpermits, facilitates, and/or otherwise contributes to or is associatedwith the transmission of messages, packets, signals, and/or other formsof information between and/or within one or more network devices.Networks may be or include a plurality of interconnected networkdevices. In some embodiments, networks may be hard-wired, wireless,virtual, neural, and/or any other configuration or type that is orbecomes known. Communication networks may include, for example, devicesthat communicate directly or indirectly, via a wired or wireless mediumsuch as the Internet, intranet, LAN, WAN, Virtual Private Network (VPN),Ethernet (or IEEE 802.3), Token Ring, or via any appropriatecommunications means or combination of communications means. Exemplaryprotocols include but are not limited to: Bluetooth™, Time DivisionMultiple Access (TDMA), Code Division Multiple Access (CDMA), GlobalSystem for Mobile communications (GSM), Enhanced Data rates for GSMEvolution (EDGE), General Packet Radio Service (GPRS), Wideband CDMA(WCDMA), Advanced Mobile Phone System (AMPS), Digital AMPS (D-AMPS),IEEE 802.11 (WI-FI), IEEE 802.3, SAP, the best of breed (BOB), and/orsystem to system (S2S).

In cases where video signals or large files are being sent over thenetwork, a broadband network may be used to alleviate delays associatedwith the transfer of such large files, however, such an arrangement isnot required. Each of the devices may be adapted to communicate on sucha communication means. Any number and type of machines may be incommunication via the network. Where the network is the Internet,communications over the Internet may be through a website maintained bya computer on a remote server or over an online data network, includingcommercial online service providers, and/or bulletin board systems. Inyet other embodiments, the devices may communicate with one another overRF, cable TV, and/or satellite links. Where appropriate, encryption orother security measures, such as logins and passwords, may be providedto protect proprietary or confidential information.

As used herein, the terms “information” and “data” may be usedinterchangeably and may refer to any data, text, voice, video, image,message, bit, packet, pulse, tone, waveform, and/or other type orconfiguration of signal and/or information. Information may compriseinformation packets transmitted, for example, in accordance with theInternet Protocol Version 6 (IPv6) standard. Information may, accordingto some embodiments, be compressed, encoded, encrypted, and/or otherwisepackaged or manipulated in accordance with any method that is or becomesknown or practicable.

As used herein, “determining” includes calculating, computing, deriving,looking up (e.g., in a table, database, or data structure),ascertaining, and/or recognizing.

A “processor” means any one or more microprocessors, Central ProcessingUnit (CPU) devices, computing devices, microcontrollers, and/or digitalsignal processors. As utilized herein, the term “computerized processor”generally refers to any type or configuration of primarily non-organicprocessing device that is or becomes known. Such devices may include,but are not limited to, computers, Integrated Circuit (IC) devices, CPUdevices, logic boards and/or chips, Printed Circuit Board (PCB) devices,electrical or optical circuits, switches, electronics, optics and/orelectrical traces. A sub-class of computerized processors, as utilizedherein, may comprise “mechanical processors” which may generallyinclude, but are not limited to, mechanical gates, mechanical switches,cogs, wheels, gears, flywheels, cams, mechanical timing devices, etc.

The terms “computer-readable medium” and “computer-readable memory”refer to any medium that participates in providing data (e.g.,instructions) that may be read by a computer and/or a processor. Such amedium may take many forms, including but not limited to non-volatilemedia, volatile media, and other specific types of transmission media.Non-volatile media include, for example, optical or magnetic disks andother persistent memory. Volatile media include DRAM, which typicallyconstitutes the main memory. Other types of transmission media includecoaxial cables, copper wire, and fiber optics, including the wires thatcomprise a system bus coupled to the processor.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, any other magneticmedium, a CD-ROM, Digital Video Disc (DVD), any other optical medium,punch cards, paper tape, any other physical medium with patterns ofholes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, a USB memory stick, adongle, any other memory chip or cartridge, a carrier wave, or any othermedium from which a computer can read. The terms “computer-readablememory” and/or “tangible media” specifically exclude signals, waves, andwave forms or other intangible or transitory media that may neverthelessbe readable by a computer.

Various forms of computer-readable media may be involved in carryingsequences of instructions to a processor. For example, sequences ofinstruction (i) may be delivered from RAM to a processor, (ii) may becarried over a wireless transmission medium, and/or (iii) may beformatted according to numerous formats, standards or protocols. For amore exhaustive list of protocols, the term “network” is defined aboveand includes many exemplary protocols that are also applicable here.

In some embodiments, one or more specialized machines such as acomputerized processing device, a server, a remote terminal, and/or acustomer device may implement the various practices described herein. Acomputer system of an insurance company may, for example, comprisevarious specialized computers that interact to perform risk assessments,insurance premium calculations, insurance product sales, geospatialvalue subject analysis and/or management, and/or value subject datasales, as described herein.

Turning first to FIG. 1, a block diagram of a system 100 according tosome embodiments is shown. In some embodiments, the system 100 maycomprise a plurality of location data devices 102 a-n. The location datadevices 102 a-n may collect and/or store data descriptive and/orindicative of a location of one or more objects or areas (such as valuesubjects). The location data devices 102 a-n may, for example, compriseone or more sensors, databases, and/or third-party data and/or sensingdevices configured and/or situated to determine location data. Accordingto some embodiments, any or all of the location devices 102 a-n may bein communication with a network 104. In some embodiments, the locationdata gathered and/or stored by one or more of the location data devices102 a-n can be queried, collected, sensed, looked-up, and/or otherwiseobtained and/or determined by a location processing device 110 (e.g.,via the network 104). The location processing device 110 may, forexample, comprise one or more computers and/or servers in communicationwith the location data devices 102 a-n (e.g., via the network 104). Thelocation processing device 110 may, in some embodiments, utilize thelocation information from the location devices 102 a-n to determineand/or define one or more certified locations (e.g., points and/orpolygons), define and/or identify one or more value subjects, and/orprovide (e.g., for sale) value subject and/or certified location data.In some embodiments for example, the location processing device 110 mayoffer the location information, certified location information, and/orvalue subject information for sale and/or subscription to variousentities, for various purposes. In some embodiments, the locationprocessing device 110 (and/or the location data devices 102 a-n) may bein communication with a database 140. The database 140 may store, forexample, location data obtained from the location data devices 102 a-n,certified location data and/or value subject data defined by thelocation processing device 110, and/or instructions that cause variousdevices (e.g., the location processing device 110 and/or the locationdata devices 102 a-n) to operate in accordance with embodimentsdescribed herein.

The location data devices 102 a-n, in some embodiments, may comprise anytype, configuration, and/or combination of sensor, computing, mobileelectronic, location-sensing (e.g., Global Positioning System (GPS)),network, user, and/or communication devices that are or become known orpracticable. The location data devices 102 a-n may, for example,comprise one or more Personal Computer (PC) devices, computerworkstations (e.g., underwriter workstations), tablet computers, such asan iPad® manufactured by Apple®, Inc. of Cupertino, Calif., and/orcellular and/or wireless telephones such as an iPhone® (alsomanufactured by Apple®, Inc.) or an Optimus™ S smart phone manufacturedby LG® Electronics, Inc. of San Diego, Calif., and running the Android®operating system from Google®, Inc. of Mountain View, Calif. In someembodiments, a location data device 102 a-n may comprise one or more ofa digital or analog camera/video device (e.g., a Closed-Circuit TV(CCTV) camera, a webcam, satellite imaging device, aerial imagingdevice, robotic imaging device, and/or a Pan-Tilt-Zoom (PTZ)-enabledcamera), an optical sensor, a laser sensor, a RADAR, LADAR, or SONARsensor, a thermal sensor, an electrical current sensor, an electroand/or magnetic field sensor, a distance sensor, an acoustic sensor, anyother type of sensor, and/or any combinations thereof. In someembodiments, the location data devices 102 a-n may comprise trackingdevices that are attached to/carried by people, e.g., cell phones orPersonal Digital Assistant (PDA) devices (and/or location determininghardware and/or software thereof or associated therewith), or the like,Radio-Frequency Identification (RFID) tags, Bluetooth® devices, or otherlocation tracking devices located on or within people or objects, or onor within clothing or items (e.g., jewelry, watches, etc.) attached topeople or objects, and capable of monitoring, storing and/ortransmitting their location.

The network 104 may, according to some embodiments, comprise a LocalArea Network (LAN; wireless and/or wired), Wide Area Network (WAN;wireless and/or wired), cellular telephone network, Bluetooth® network,Near-Field Communication (NFC) network, and/or Radio Frequency (RF)network with communication links between the location data devices 102a-n, the location processing device 110, and/or the database 140. Insome embodiments, the network 104 may comprise direct communicationslinks between any or all of the components 102 a-n, 110, 140 of thesystem 100. The location processing device 110 may, for example, bedirectly interfaced or connected to the database 140 via one or morewires, cables, wireless links, and/or other network components, suchnetwork components (e.g., communication links) comprising portions ofthe network 104. In some embodiments, the network 104 may comprise oneor many other links or network components other than those depicted inFIG. 1. A location data device 102 a-n may, for example, be connected tothe location processing device 110 via various cell towers, routers,repeaters, ports, switches, and/or other network components thatcomprise the Internet and/or a cellular telephone (and/or PublicSwitched Telephone Network (PSTN)) network, and which comprise portionsof the network 104.

While the network 104 is depicted in FIG. 1 as a single object, thenetwork 104 may comprise any number, type, and/or configuration ofnetworks that is or becomes known or practicable. According to someembodiments, the network 104 may comprise a conglomeration of differentsub-networks and/or network components interconnected, directly orindirectly, by the components 102 a-n, 110, 140 of the system 100. Thenetwork 104 may comprise one or more cellular telephone networks withcommunication links between the location data devices 102 a-n and thelocation processing device 110, for example, and/or may comprise theInternet, with communication links between the location data devices 102a-n and the database 140, for example.

According to some embodiments, the location processing device 110 maycomprise a device (or system) owned and/or operated by or on behalf ofor for the benefit of an insurance company and/or data provider. Theinsurance company may utilize location information and/or certifiedlocation information, in some embodiments, to manage, analyze, design,rate, price, and/or otherwise structure insurance and/or otherunderwriting products, and/or define, facilitate, and/or influence otherinsurance and/or business processes. Certified location information may,for example, enhance the accuracy of insurance risk assessments and thuslead to more profitable and/or reliable insurance product offerings. Insome embodiments, certified location information may be utilized toprovide discounted premiums and/or other incentives or benefits toinsurance customers. An insurance company may provide a discount to acustomer willing to allow the insurer (or a third party benefiting theinsurer) access to certified location information (and/or access tolocation information via which the certified location information may bedetermined). Discounts may be provided, for example, for various levelsof increasing detail of location information that a customer is willingto provide to an insurance company. According to some embodiments,location and/or certified location information may be utilized toprovide value subject identification and/or definition functionality toend-users. One or more of the location data devices 102 a-n may beutilized by a user, for example, to access stored value subject data(e.g., stored in the database 140) via the location processing device110 (e.g., for a fee).

In some embodiments, the database 140 may comprise any type,configuration, and/or quantity of data storage devices that are orbecome known or practicable. The database 140 may, for example, comprisean array of optical and/or solid-state hard drives configured to storelocation data provided by (and/or requested by) the location datadevices 102 a-n, certified location data (e.g., defined and/ordetermined by the location processing device 110), value subject data,and/or various operating instructions, drivers, etc. While the database140 is depicted as a stand-alone component of the system 100 in FIG. 1,the database 140 may comprise multiple components. In some embodiments,a multi-component database 140 may be distributed across various devicesand/or may comprise remotely dispersed components. Any or all of thelocation data devices 102 a-n may comprise the database 140 or a portionthereof, for example, and/or the location processing device 110 maycomprise the database or a portion thereof.

In some embodiments, various user interfaces (not explicitly shown inFIG. 1; such as the interfaces 620, 920, 1520 of FIG. 6, FIG. 9, and/orFIG. 15 herein) may be utilized to enhance the ability to comprehend orutilize location data, value subject data, and/or certified locationdata (which may often represent complex geo-spatial relationships). Anapplication for a mobile device (such as an Apple® iPhone® application,for example) may, in some embodiments, provide a visual indication ofcertified location data and/or value subject data gathered by (and/orfrom) the location data devices 102 a-n and/or processed by the locationprocessing device 110. According to some embodiments, certified locationdata and/or value subject data may be depicted visually on a map and/oras a layer on a map, such as may be provided, for example, by Google®Maps. Such visually-depicted data may comprise real-time, delayed,historical (e.g., historical aggregate, average, trend), pre-defined,and/or predicted data. In such a manner, for example, a customer ofcertified location and/or value subject data may utilize a mobile and/orother device to view a map (and/or other graphical depiction) ofcertified locations and/or value subjects and utilize the map to inform,facilitate, and/or conduct business decision-making processes (such asrisk assessments and/or underwriting decisions). In some embodiments,the customer/user may utilize such a map and/or interface to define avalue subject and/or define an area for which data (e.g., certifiedlocation data and/or value subject data) is desired. As describedherein, such a definition may comprise an indication of one or moregeospatial points, lines, and/or polygons (e.g., provided as input viathe interface).

Referring now to FIG. 2A, FIG. 2B, FIG. 2C, and FIG. 2D, a top view ofan example location 200, a first perspective view of the examplelocation 200, a zoomed-in top view of a portion 202 of the examplelocation 200, and a second perspective view of the example location 200according to some embodiments are shown, respectively. The examplelocation 200 may comprise a city block and/or a portion of a certainarea or zone such as a census tract, voting district, United StatesGeological Survey (USGS) quadrangle, media market, and/or zip code, forexample. In some embodiments, as depicted in FIG. 2A, FIG. 2B, FIG. 2C,and/or FIG. 2D, for example, the example location 200 may comprise aplurality of polygons 210 a-c (e.g., land parcels and/or valuesubjects). The polygons 210 a-c may, in some embodiments, be defined bymunicipalities and comprise tax and/or assessment parcels. In someembodiments, polygons 210 a-c may be defined by other entities (e.g., byan insurance company employee) and/or be based on other geographic,economic, social, political, and/or other demographic and/or businessfactors (e.g., based on risk and/or other underwriting characteristics).

According to some embodiments, the polygons 210 a-c may comprise and/orbe defined by one or more vertices 212 (depicted in FIG. 2A and FIG. 2C,but not replicated in FIG. 2B or FIG. 2D, for clarity). A first polygon210 a may, for example, be defined and/or bounded by a first vertex 212a-1 (depicted at the upper-left of the first polygon 210 a), a secondvertex 212 a-2 (depicted at the upper-right of the first polygon 210 aand bordering with a second polygon 210 b), a third vertex 212 a-3(depicted at the lower-right of the first polygon 210 a and alsobordering with the second polygon 210 b), and a fourth vertex 212 a-4(depicted at the lower-left of the first polygon 210 a). In someembodiments, the second polygon 210 b and/or a third polygon 210 c maybe defined by and/or comprise vertices 212 b-1, 212 b-2, 212 b-3, 212b-4 and 212 c-1, 212 c-2, 212 c-3, 212 c-4, respectively. In some cases,such as in the case that the third vertex 212 a-3 of the first polygon210 a and the fourth vertex 212 b-4 of the second polygon 210 bcoincide, a single graphical point may be described and/or defined withreference to either or both of the overlapping vertices 212 a-3, 212b-4. Although each of the polygons 210 a-c is depicted in FIG. 2A, FIG.2B, FIG. 2C, and/or FIG. 2D as comprising a rectilinear shape, a polygon210 a-c may comprise any type and/or configuration of shape that is orbecomes known or practicable, and may accordingly comprise and/or bedefined by any number of vertices 212. According to some embodiments,such as in the case that a polygon 210 a-c comprises an ellipse, circle,and/or other non-rectilinear shape (not shown in FIG. 2A or FIG. 2B),one or more of the vertices 212 a-1, 212 a-2, 212 a-3, 212 a-4, 212 b-1,212 b-2, 212 b-3, 212 b-4, 212 c-1, 212 c-2, 212 c-3, 212 c-4 may beutilized to identify any desired geometric point, midpoint, center,centroid, intersection, inflection, tangent, moment, etc.

In some embodiments, other (e.g., non-vertex) points 214 may beassociated with and/or define the polygons 210 a-c. Points 214 mayrepresent and/or define, for example, non-vertex attributes of a givenpolygons 210 a-c. In some embodiments, such as in the case that thenon-vertex attribute comprises a geometric attribute, the points 214 mayrepresent and/or define one or more of a centroid of a polygon 210 a-c,an offset from a corner or side of a polygon 210 a-c, a midpoint of aline segment connecting two vertices 212, and/or may identify one ormore features of a polygon 210 a-c (e.g., topological locations, zoneboundary locations, structure locations, user-defined locations, and/orother reference locations). In some embodiments, the points 214 may bedefined by one or more coordinates such as ‘x’, ‘y’, and/or ‘z’coordinates, GPS coordinates, Latitude and Longitude coordinates, etc.According to some embodiments, one or more three-dimensional shapes(e.g., polyhedrons, ellipsoids, pyramids, cylinders, and/or combinationsthereof, etc.) may be utilized to identify and/or locate one or morestructures. A structure may be completely bounded and/or enclosed orencompassed by a three-dimensional polygon shape and/or representation,for example, or may be partially bounded, enclosed, and/or encompassed.Various sensing devices such as a Light Detection and Ranging (LiDAR)device may be utilized in some embodiments to acquire and/or verify datadescriptive of such three-dimensional polygons. In some embodiments, thepoints 214 may be utilized to define and/or identify a particularpolygon 210 a-c (and/or a feature thereof).

According to some embodiments, a first point 214 a-1 may be identifiedas existing within the first polygon 210 a. The first point 214 a-1 may,for example, comprise a single coordinate point of-record (e.g., in amunicipal assessor's office or defined by a user via a GUI) inassociation with the first polygon 210 a. In some embodiments, a secondpoint 214 a-2 may identify a feature of the first polygon 210 a. Asdepicted at the example location 200 of FIG. 2A, FIG. 2B, FIG. 2C,and/or FIG. 2D, for example, the polygons 210 a-c may comprise and/orhave situated thereupon one or more structures 216. The second point 214a-2, for example, may identify a location of a first structure 216 a-1(e.g., a building such as a warehouse) of the first polygon 210 a. Insome embodiments, one or more structures 216 may exist on a polygon 210a-c. In some embodiments, a structure such as a second structure 216 a-2(e.g., a building such as an apartment building) may extend acrossand/or exist in multiple polygons 210 a-c (in the case of the secondstructure 216 a-2, for example, existing in both the first and secondpolygons 210 a-b—e.g., first and second value subjects). According tosome embodiments, the example location 200 may comprise one or morecustomer locations 218 (e.g., areas, objects, value subjects, and/orpoints or boundaries associated with one or more individuals and/orentities such as a customer of a business).

In some embodiments, a first customer location 218-1 may identify aportion of the second structure 216 a-2, within the first polygon 210 a,where a first customer (not explicitly shown) resides, does business,etc. In some embodiments, a third point 214 b in the second polygon 210b may identify a second location 218-2 of a second customer (e.g., acustomer of an insurance business as described with respect to someembodiments herein). As depicted, for example, the third point 214 b mayidentify the second location 218-2 in the second structure 216 a-2 onthe second polygon 210 b. According to some embodiments, the third point214 b and/or the second location 218-2 of the second customer maycoincide with, conflict with, and/or be equivalent or similar to a thirdlocation 218-3 of a third customer. As depicted in FIG. 2B, for example,the second location 218-2 may define an area of an upper floor of thesecond structure 216 a-2, while the third location 218-3 may define anarea of a lower floor of the second structure 216 a-2, beneath thesecond location 218-2. In some embodiments, the third point 214 b may beassociated with elevation, height, and/or floor data that defines thesecond location 218-2. In some embodiments, a fourth location 218-4 of afourth customer may be identified in another portion of the secondstructure 216 a-2 on the second polygon 210 b.

In some embodiments, one or more points 214 may coincide with, identify,and/or define one or more vertices 212 of the polygons 210 a-c. Asdepicted with respect to the third polygon 210 c, for example, a fourthpoint 214 c-1 may define a location of the first vertex 212 c-1 of thethird polygon 210 c and/or a fifth point 214 c-2 may define a locationof the third vertex 212 c-3 of the third polygon 210 c. In such amanner, for example, coordinate data associated with the fourth andfifth points 214 c-1, 214 c-2 may define a geospatial relationshipbetween the third polygon 210 c and other geospatially referencedobjects and/or areas (e.g., the other polygons 210 a-b).

FIG. 2A and FIG. 2B are generally provided to illustrate (via theexample location 200) various difficulties associated with identifying,defining, and/or otherwise determining certified locations and/or valuesubjects—especially with respect to customers of a business. While muchdata regarding the example location 200 may exist, for example, suchdata is generally stored and accessible only to the extent determineduseful by any entity that gathered or obtained such data. The data isalmost certainly not standardized and often overlaps or conflicts withother data sets utilized for various purposes. The data may beincomplete, inaccurate, and/or may include (or lack) alternativespellings and/or identifiers (e.g., “vanity” addresses). These and otherissues substantially hinder determination of certified locations and/orvalue subjects, particularly certified locations and/or value subjectsof customers and certified locations and/or value subjects defined byend-users.

As an example, surveying data regarding the first polygon 210 a may bememorialized on a municipal map (e.g., a zoning map) that definesgeo-referenced coordinates for each vertex 212 a of the first polygon210 a. The map may also define coordinates for each corner of the firststructure 216 a-1. This data (e.g., the coordinates) is merely displayedon a static map, however, and is not stored in a database or otherwiseaccessible to being queried or utilized electronically. Nor is this datatypically stored in association with other location information, such asstreet address data. Thus, in the case an address of the first customerat the first location 218-1 is known, the zoning information and/orcoordinate data is not likely known nor readily identifiable orsearchable. Similarly, if a coordinate of the first location 218-1 isknown, street address information may not be discernible based upon thatinformation. Further, non-vertex points 214 such as the first and secondpoints 214 a-1, 214 a-2 are likely not noted on the zoning maps. Thus,if the first customer (or an information provider or providers who areproviding information about that customer or that address, polygon,value subject, parcel, and/or structure) provides one or more of thefirst and second points 214 a-1, 214 a-2 as coordinate informationdescriptive of the first location 218-1 (e.g., via a mobile device),there may be no efficient manner to determine from such coordinateinformation a street address (or other valid value subject identifier)of the customer, much less which polygon 210 a-c the customer is on,much less which structure 216 a-c the customer is in.

Even in the case that address information is available for a particularpolygon 210 a-c, the address information generally comprises a range ofstreet addresses that have been allocated to that particular polygon 210a-c—some or many of which may not be in use. Street address informationfor the first polygon 210 a may indicate, for example, that addressesfrom one (1) to one hundred (100) “Main Street” are assigned to thefirst polygon 210 a. In the case that it is known that the firstlocation 218-1 of the first customer has a street address of fifty (50)“Main Street”, the street address may be utilized to determine that thefirst location 218-1 is within the first polygon 210 a. The streetaddress does not, however, allow for the determination of which of thefirst structure 216 a-1 and the second structure 216 a-2 the firstlocation 218-1 is within, nor does it provide any information regardinggeospatial orientation of the first location 218-1 (e.g., the streetaddress and/or the polygon and/or parcel information do not allow forthe determination of how far the first location 218-1 is from the secondor third locations 218-2, 218-3). In some embodiments, a polygon 210 a-cmay only be associated with one of a plurality of addresses located atthe polygon 210 a-c. The polygon 210 a-c may have a “legal” and/or taxaddress, for example, and all other mailing addresses at the polygon 210a-c may not be “legal” and/or tax addresses, and may accordingly not bestored in association with the polygon 210 a-c.

In some cases, the first polygon 210 a may only be associated with asingle exterior point or coordinate 214 a-3 that does not fall withinthe actual boundaries of the first polygon 210 a. The exterior point 214a-3 may, for example, have been assigned to represent the location ofthe first polygon 210 a at a time when geospatial accuracies were lowerand/or may represent the best available geospatial informationdescriptive of the first polygon 210 a.

Similarly, while street address information may be utilized to determinethat the first location 218-1 is within the first polygon 210 a and thefourth location 218-4 is within the second polygon 210 b, there iscurrently no manner to discern that both locations 218-1, 218-4 arewithin the same second structure 216 a-2. Nor does knowledge that thesecond location 218-2 and the fourth location 218-4 are in the secondpolygon 210 b allow for specific identification of a certified locationand/or value subject for either respective customer, as there is noinformation distinguishing between the second structure 216 a-2 and thethird structure 216 b (e.g., particularly in the case that the secondand third structures 216 a-2, 216 b are connected and/or are otherwiseconsidered to comprise the same and/or overlapping street addresses). Insome embodiments, structures 216 (and/or polygons 210 a-c) may not beassociated with street address information. In the case that a structurecomprises a structure not operated as a home or business, such as thefourth structure 216 c for example, no mailing address may be required,assigned, and/or otherwise applicable. Similarly, a polygon 210 a-c maycomprise and/or define a non-address area such as a field, stream,intersection, zone, region, etc.

In some embodiments, accuracies, confidence levels, and/or probabilitiesassociated with points 214 (and/or other location data) may be utilizedto make determinations regarding certified location and/or value subjectdata. As depicted in FIG. 2C, for example, the portion 202 of theexample location 200 may comprise three different points 214 labeled“A”, “B”, and “C”, respectively. In some embodiments, the points “A”,“B”, and/or “C” may be similar to and/or associated with one or more ofthe points 214 depicted in FIG. 2A. The second point 214 a-2 of FIG. 2Amay, for example, be based on and/or otherwise associated with the point“A” of FIG. 2C. In some embodiments for example, the point “A” maycomprise a representation of location information acquired with respectto and/or determined to be descriptive of the first structure 216 a-1.

According to some embodiments, the point “A” may be determined to beassociated with one or more confidence levels “A1”, “A2”, and/or “A3”(e.g., areas, zones, and/or volumes). A first set and/or pre-determineddistance/radius from the point “A” may be determined to represent afirst confidence level “A1”, a second set and/or pre-determineddistance/radius from the point “A” may be determined to represent asecond confidence level “A2”, and/or a third set and/or pre-determineddistance/radius from the point “A” may be determined to represent athird confidence level “A3”. In some embodiments, the confidence levels“A1”, “A2”, “A3” may be utilized to determine a likelihood and/orprobability (e.g., a weight, score, etc.) that the point “A” accuratelyrepresents and/or identifies (e.g., locates) the first structure 216a-1. In the case that it is known that the point “A” is supposed torepresent the first structure 216 a-1, for example, because the secondconfidence level “A2” overlaps with a portion of the first structure 216a-1, an inference may be made that the point “A” (and/or relatedlocation information) is not as accurate is it could be and/or asdesired (such as in a case where the first confidence level “A1”overlapped with a portion of the first structure 216 a-1), but is notinaccurate. In some embodiments, a score and/or weight may be assignedto the point “A” based on one or more of the confidence levels “A1”,“A2”, and/or “A3” and their relation to various location components,such as polygons 210 a-c, structures 216 a-1, 216 a-2, 216 b and/orcustomer location 218-1.

In some embodiments, inaccuracy may be assumed in a case where the point“A” is supposed to represent the second structure 216 a-2. As the secondstructure 216 a-2 falls outside of even the third confidence level “A3”,it may be assumed that the point “A” is inaccurate or suspect (e.g., alow likelihood of being accurate and/or useful). In some embodiments, arelative comparison of the proximity of the point “A” to the differentstructures 216 a-1, 216 a-2 may be utilized to determine which (if any)structure 216 a-1, 216 a-2 the point “A” is likely associated with(and/or descriptive of). As the point “A” is closer to the firststructure 216 a-1 than the second structure 216 a-2 (utilizing theconfidence levels “A1”, “A2”, and/or “A3” or straight distancemeasurements), for example, it may be determined that the point “A” isdescriptive of and/or associated with the first structure 216 a-1 (asopposed to the second structure 216 a-2).

According to some embodiments, such as in the case of the point “B”,confidence levels “B1”, “B2”, and/or “B3” may be utilized to determine alikelihood of accuracy (and/or association) of the point “B”. As eventhe tightest depicted confidence level “B1” overlaps or includesportions of both the first polygon 210 a and the third polygon 210 c,for example, it may be determined that point “B” has a low confidencescore/rank due to the close proximity of the various polygons 210 a, 210c. In some embodiments, a rank, weight, and/or score may be assigned tothe point “B” with respect to each proximate polygon 210 a-c, structure216 a-1, 216 a-2, 216 b, and/or customer/business location 218-1. Thepoint “B” may be determined to have a qualitative rank of “VERY HIGH” ora quantitative rank of “0” with respect to the third polygon 210 c, forexample, may be determined to have a “HIGH” and/or “1” rank/score withrespect to the first polygon 210 a, and/or may be determined to have a“LOW” and/or “14” rank/score with respect to the second polygon 210 b.According to some embodiments (as described in more detail hereinafter),such confidence levels “B1”, “B2”, “B3” (and/or “A1”, “A2”, “A3”),weights, scores, and/or rankings may be utilized with respect to points214 (such as points “A” and/or “B”) and/or other location informationfor comparative analysis to determine certified location and/or valuesubject data.

In some embodiments, such as depicted with respect to point “C” in FIG.2C, confidence levels “C1”, “C2”, “C3” may be defined, expressed, and/ordepicted in various manners (e.g., not limited to radial and/or circularprojects and/or areas). Rectilinear confidence levels “C1”, “C2”, “C3”as depicted in FIG. 2C, for example, may be useful in various cases suchas in the case that the point “C” is known (or believed) to be withinthe second structure 216 a-2. In the case that data descriptive of thepoint “C” is received from a field agent and/or customer known to have acustomer location 218-1, for example, the confidence levels “C1”, “C2”,“C3” may be utilized to determine (i) whether the customer location218-1 information already on file is accurate, (ii) a layout and/orconfiguration of the customer location 218-1 (and/or the secondstructure 216 a-2), and/or (iii) how likely it is that the customerlocation 218-1 is within the second structure 216 a-2 (e.g., as opposedto the third structure 216 b). Similarly, confidence levels “C1”, “C2”,“C3” may comprise height and/or elevation data and/or extend into,cross, and/or define one or more horizontal planes (e.g., representingdifferent floors of the second structure 216 a-2). In such a manner, forexample, the confidence levels “C1”, “C2”, “C3” may be utilized to rank,score, and/or determine a likelihood that the customer location 218-1 ison a given floor of the second structure 216 a-2 (and/or to assist indetermining features of the second structure 216 a-2, such as number offloors).

In some embodiments, other location-related information may be depictedand/or utilized in conjunction with the example location 200. Asdepicted in FIG. 2D, for example, one or more of a fall risk zone 220, aterrorist risk zone 222, and/or a weather risk zone 224 may beassociated with the example location 200, one or more of the polygons210 a-c, one or more of the structures 216 a-1, 216 a-2, 216 b, 216 c,and/or one or more customer and/or business locations 218 (most notreproduced in FIG. 2D for clarity of depiction). According to someembodiments, such risk zones 220, 222, 224 may be determined,calculated, depicted, and/or utilized based on certified location and/orvalue subject data gathered, compiled, aggregated, analyzed, and/orotherwise processed as described herein. Once the spatial (e.g.,geo-spatial and/or relational) relationships of the polygons 210 a-c,the structures 216 a-1, 216 a-2, 216 b, 216 c, and/or the businesslocations 218 are determined, for example, different risks, likelihoodsof risk, and/or risk scores or weights may be determined and/orvisualized.

With respect to the fall risk zone 220, for example, assuming the heightof the fourth structure 216 c is known or determined, the fall risk zone220 may be calculated, graphed, and/or depicted to define a fall riskarea 220 a (and/or volume) that is subject to risk due to objectsfalling from the fourth structure 216 c (and/or due to a collapse of thefourth structure 216 c itself). In some embodiments, such as depicted inFIG. 2D, in the case that the fall risk area 220 a (and/or volume)overlaps with and/or includes the first structure 216 a-1, a structurerisk area 220 b may be defined that represents an area, portion, and/orvolume of the first structure 216 a-1 that is subject to risk due topotential problems with the fourth structure 216 c. In some embodiments,the structure risk area 220 b may be utilized to determine a likelymagnitude of potential loss occurring to the first structure 216 a-1 dueto the fourth structure 216 c. In some embodiments, such loss predictiondeterminations may include analysis of other data descriptive of thefourth structure 216 c. In the case that the fourth structure 216 ccomprises a radio tower or antenna mast, for example, and assuming thestructure risk area 220 b as depicted in FIG. 2D, it may be determinedthat likely damage to the first structure 216 a-1 may be limited toroofing and/or roof member damage (e.g., as opposed to severe structuraldamage, internal building system losses, etc.).

In some embodiments, terrorist risk zone 222 may be utilized todetermine how relationships in location information (e.g., certifiedlocation and/or value subject information) are likely to affect risk. Asdepicted in FIG. 2D, for example, in the case that an explosion and/orother catastrophic accident and/or terrorist event in front of thesecond structure 216 a-2 (e.g., on “Main St.”) is modeled to occur, theterrorist risk zone 222 may be defined and/or depicted. In someembodiments, such as in the case that the terrorist risk zone 222comprises a hemispherical shape as depicted, a ground blast zone 222 a,a building surface blast zone 222 b, a peripheral building surfacedamage zone 222 c, and/or a structural failure zone 222 d may bedetermined.

According to some embodiments, such as in the case that the firstpolygon 210 a is associated with and/or comprises mailing addresses on“Main St.” and the second polygon 210 b is associated with and/orcomprises mailing addresses on “Side St.”, in the absence of certifiedlocation and/or value subject information that allows visualization(and/or computation) based on the actual physical and/or locationalrelationships as depicted at the example location 200, one would not beable to easily determine that the terrorist risk zone 222 (due to athreat/event on “Main St.”) may impact addresses on “Side St.” Asdepicted, for example, unless it is known (e.g., via utilization ofcertified location and/or value subject systems and methods describedherein) that the second structure 216 a-2 comprises addresses on both“Main St.” and “Side St.”, the potential risk of loss may be grosslyunderestimated. Assume, for example, that an insurance company has manycustomers (not shown) in the second structure 216 a-2 situated in/on thefirst polygon 210 a (and accordingly all having mailing, business,domicile, and/or tax addresses on “Main St.”). A new potential customercomes forth and requests an insurance policy protecting theirhome/business located at “2 Side St.”—and situated as depicted at thefifth customer location 218-5 in FIG. 2D. Without access to certifiedlocation information and the various relationships associated therewithas described herein, the insurance company may see no particular risk inunderwriting a policy for the new customer (even if the potential threatrepresented by the terrorist risk zone 222 is known), and mayaccordingly take on the new account/policy. Similarly, in the case thata data customer and/or risk assessment agent attempts to retrieve dataassociated with the fifth customer location 218-5, such as to determinea risk associated with the fifth customer location 218-5, typicalsystems would require an address to be entered and stored data inassociation with the address would then be retrieved. According to someembodiments herein, the agent/customer could instead utilize aninterface (not explicitly shown in FIG. 2A, FIG. 2B, FIG. 2C, and/orFIG. 2D; such as the interfaces 620, 920, 1520 of FIG. 6, FIG. 9, and/orFIG. 15 herein) to define one or more points, lines, and/or polygonsthat define an area (e.g., a value subject) for which data is desired.The input location data could then, for example, be mapped/transformedinto a desirable geospatial data format/structure/layout, and utilizedto query associated data.

With the advantage of embodiments described herein, the insurancecompany could realize that the fifth customer location 218-5 actuallyfalls within the terrorist risk zone 222 (and/or the structural failurezone 222 d associated therewith) due to a potential “Main St.” event.Such information may accordingly be utilized to determine, with a muchhigher degree of accuracy and/or confidence, whether underwriting such apolicy would be within the risk appetite for the insurance company.Similarly, existing policies and/or exposures may be reviewed todetermine an overall risk/loss level associated with any given event(such as represented by the terrorist risk zone 222). Such informationmay then be utilized to reduce risk (e.g., by modifying, cutting, and/orfreezing policies and/or policy underwriting) to within acceptablelimits (e.g., presuming that previous “blind” practices have resulted inoverexposure due to incomplete location relationship information). Suchinformation may also or alternatively be utilized to redefine existingvalue subject data and/or boundaries and/or to define new value subjectdata, e.g., via point, line, and/or polygon data entered by a user viaan interface (such as the interfaces 620, 920, 1520 of FIG. 6, FIG. 9,and/or FIG. 15 herein).

In some embodiments, the weather risk zone 224 may be utilized todetermine locational relationship-based damage and/or risk likelihoods.In the case of a wind, hail, storm surge, and/or other weather events,such as depicted by the weather risk zone 224, for example, it may bedetermined that certain locational elements, such as structures 216 a-1,216 a-2, 216 b, may be more or less likely to result in claims, damage,and/or losses due to a particular event. Upper-level floors of the thirdstructure 216 b may be exposed to the weather risk zone 224, forexample, while lower-level floors may be shielded by the secondstructure 216 a-2. Such risk information may be utilized to develop riskscores for different certified locations and/or value subjects such as,for example, different apartments/businesses in the same structure 216a-1, 216 a-2, 216 b. In the absence of certified location informationand/or relationships, too much or too little risk exposure may beexperienced with respect to underwriting products sold for the examplelocation 200. In the case that the weather risk zone 224 represents aknown high likelihood of wind damage, for example, the risk coverageassociated with policies written for customers in the third structure216 b may be lower than desired (e.g., the shielded lower floors havelower risk), resulting in lower revenue and/or profits than arepossible/desirable. The availability and knowledge of the locationalrelationships based on certified location data allow for such otherwisemissed revenues and/or profits to be realized while maintaining desiredlevels of risk exposure. Certified location data also may, for example,allow for the dynamic, customized, and/or user-initiated definition ofvalue subjects and/or the retrieval of risk (and/or other) dataassociated therewith.

According to some embodiments, any or all of the components 210 a-c, 212a-c, 214 a-c, 216 a-c, 218, 220, 222, 224 of and/or associated with theexample location 200 may be similar in configuration and/orfunctionality to any similarly named and/or numbered componentsdescribed herein. Fewer or more components 210 a-c, 212 a-c, 214 a-c,216 a-c, 218, 220, 222, 224 and/or various configurations of thecomponents 210 a-c, 212 a-c, 214 a-c, 216 a-c, 218, 220, 222, 224 may beincluded in and/or in association with the example location 200 withoutdeviating from the scope of embodiments described herein. Whilemultiples of some components 210 a-c, 212 a-c, 214 a-c, 216 a-c, 218 aredepicted and while single instances of other components 218, 220, 222,224 are depicted, for example, any component 210 a-c, 212 a-c, 214 a-c,216 a-c, 218, 220, 222, 224 depicted in and/or in association with theexample location 200 may comprise a single object and/or component, acombination of objects and/or components 210 a-c, 212 a-c, 214 a-c, 216a-c, 218, 220, 222, 224, and/or a plurality of objects and/orcomponents, as is or becomes desirable and/or practicable.

Turning now to FIG. 3A, FIG. 3B, FIG. 3C, and FIG. 3D, block diagrams ofvarious components of a system 300 in accordance with some embodimentsare shown. In FIG. 3A, a first example data storage structure 340 a ofthe system 300 according to some embodiments is shown. The first exampledata storage structure 340 a may, for example, depict how datarepresenting some of the various aspects and/or objects and/orcomponents depicted in FIG. 2A, FIG. 2B, FIG. 2C, and/or FIG. 2D may bestored. In some embodiments, the first example data storage structure340 a may comprise a location ID field 344 a-1, a structure ID field 344a-2, an address field 344 a-3, a city field 344 a-4, a state field 344a-5, a zip field 344 a-6, a structure polygon field 344 a-7, a parcelpolygon field 344 a-8, a point field 344 a-9, and/or a point confidencefield 344 a-10. As depicted, for example, a particular structure at “123Main Street” in “Anytown, VA” may be defined by a series of coordinatesstored in the structure polygon field 344 a-7 (any or all of which maybe similar to the vertices 212 and/or points 214 of FIG. 2A and/or FIG.2C), thereby defining a polygon representing (and/or defining) thestructure. In some embodiments, a point may be associated with theaddress and/or the structure and coordinate data for the point may bestored in the point field 344 a-9. According to some embodiments, aconfidence level (and/or likelihood or probability) descriptive of theassociation between the point and the structure and/or the address maybe stored in the point confidence field 344 a-10 (such as datadescriptive of one or more of the confidence levels, scores, ranks,and/or weights “A1”, “A2”, “A3”, “B1”, “B2”, “B3”, “C1”, “C2”, and/or“C3” of FIG. 2C). In some embodiments, although not explicitly shown inFIG. 3A, one or more other confidence levels may also or alternativelybe stored, such as representing a confidence level of an associationbetween a structure and a parcel and/or representing a confidence levelassociated with any particular polygon (e.g., a parcel and/or structuresboundaries)—such as data descriptive of one or more of the confidencelevels, scores, ranks, and/or weights “A1”, “A2”, “A3”, “B1”, “B2”,“B3”, “C1”, “C2”, and/or “C3” of FIG. 2C. In some embodiments, such asin the case that the first example data storage structure 340 a storescertified location and/or value subject information, the location IDfield 344 a-1 may comprise a certified location and/or value subjectidentifier and/or certificate, such as a unique, encoded, and/orencrypted identifier.

In some embodiments, location and/or location relationship data may begathered and/or stored in a variety of ways. In FIG. 3B, for example, ablock diagram of an example location data set 302 (e.g., comprising datadescriptive of a polygon 310, one or more points 314 a-d, and/or one ormore structures 316 a-c) mapped to a second example data storagestructure 340 b of the system 300 according to some embodiments isshown. Data descriptive of (and/or derived from) the various points 314a-d, structures 316 a-c, and/or polygon 310, for example, may be mappedto and/or otherwise stored in a location data table 344 b-1. In someembodiments, the location data table 344 b-1 may be utilized to develop,calculate, and/or otherwise define or determine a certified locationand/or value subject metric such as the depicted certified locationcertificate number 344 b-2. The certified location certificate number344 b-2 may be similar to the location ID field 344 a-1, for example,and/or may otherwise comprise a unique identifier of a particulargeo-location (such as a value subject). In some embodiments, thelocation data table 344 b-1 may be configured to store informationdescriptive of one or more “alias/alternates”, as depicted. An aliasand/or alternate may, for example, comprise an alias for an address(and/or structure), such as a ‘vanity’ address or local or informal nameor variant, and/or a common (in general and/or with respect to aspecific individual—such as a specific underwriter and/or agent)misspelling and/or mistake. In such a manner, for example, one or moreusers may be permitted to interface with the system 300 utilizing one ormore informal, vanity, and/or incorrect address, structure, and/or otheridentifiers, while maintaining the ability of the system 300 to identifyunique, e.g., certified, locations.

According to some embodiments, such as depicted in FIG. 3C, a blockdiagram of a third example data storage structure 340 c of the system300 according to some embodiments is shown. The third example datastorage structure 340 c may, for example, be utilized in conjunctionwith the location data table 344 b-1 and/or the certified locationcertificate number 344 b-2 to determine various aggregate insurance(and/or other business) metrics for the particular certified locationcertificate number 344 b-2 (e.g., and accordingly for the particularcertified location and/or value subject for which the certified locationcertificate number 344 b-2 is descriptive). The third example datastorage structure 340 c may, in some embodiments (such as depicted inFIG. 3C), comprise an insurance account numbers field 344 c-1, a claimlosses field 344 c-2, and/or an other insurance account informationfield 344 c-3. The certified location certificate number 344 b-2 (and/orthe location data table 344 b-1) may be utilized, for example, todetermine aggregate losses (actual and/or predicted), exposure and/orrisk levels, and/or other certified location-based and/or valuesubject-based metrics for any given polygon, parcel, structure, etc.Such information may then be utilized, in some embodiments, to provideand/or sell a subset of location data to a user and/or to determinewhether and/or to what extent (or on what terms) an insurance policyand/or other underwriting product should be written, sold, re-written,renewed, modified, etc.

In some embodiments, various rules and/or logic may be implemented,consulted, defined, and/or determined with respect to determiningwhether a given location is unique and/or whether and/or to what extentthe location bears a relationship to one or more other locations.Referring to FIG. 3D, for example, a block diagram of an example ruleset 342 of the system 300 according to some embodiments is shown. Theexample rule set 342 may, for example, provide guidance regarding howlikely a particular outcome may be with respect to incoming and/orstored data. Assume, for example, that data descriptive of one or morelocations and/or locational elements is already stored in a database(e.g., one or more of the example data storage structures 340 a-c;otherwise, not shown in FIG. 3A, FIG. 3B, FIG. 3C, and/or FIG. 3D). Thedata may define one or more polygons, value subjects, parcels, points,structures, addresses, and/or customer locations. In some embodiments,new information may be received, such as with respect to a potential newcustomer, account, etc.

According to some embodiments, the new information may be descriptive ofa particular building (e.g., as depicted with respect to the examplerule set 342; or a value subject in some embodiments). The building mayaccordingly be considered a “candidate building” (e.g., a candidate fora new policy and/or a candidate for being a new building not yetrepresented in and/or by the stored data). As depicted in FIG. 3D, thecandidate building's name may be compared (in accordance with theexample rule set 342) to existing/stored building names. Similarly, thecandidate building address may be compared to existing/stored addresses.Inferences, probabilities, decisions, and/or decision inputs mayaccordingly be defined based on such comparisons. For example, in thecase that the candidate building name is the same as (and/orsubstantially similar to; e.g., spelling and/or abbreviation variants)one already stored in a database, and the candidate building addressmatches (and/or substantially matches; e.g., spelling and/orabbreviation variants) the address for the similarly-named building, itcan be inferred that the most common conclusion would be that thebuildings are the same. In some embodiments, in such a case, the examplerule set 342 may dictate that the two buildings should be considered thesame. In which case, for example, any new data may be merged with and/orreconciled with existing/stored data with respect to the building.

In some embodiments, such as in the case that the candidate buildingname is the same/similar to a known building and the addresses are alsothe same/similar, this may be considered a “less common” indicator thatthe buildings are different. The buildings in such a case could bedifferent, such as in the case that the name provided is “residencehall” and, for example, many “residence hall” buildings exist on a givencollege campus. The probabilities, weights, and/or logic associated withsuch “most common” and/or “less common” results based on a given dataset may be utilized, in some embodiments, to inform a rules-baseddecision making process regarding whether the incoming data is likely tobe descriptive of a location for which data is already stored. In such amanner, for example, certified location data and/or value subject datamay be supplemented with incoming data to expand the certified locationand/or value subject data set based on determinations regarding whetherthe incoming data is indeed ‘new’. According to some embodiments, newand/or conflicting data may be processed by and/or through one or morerules (such as the example rule set 342) to correct errors in and/orupdate existing certified location and/or value subject data with newer,more accurate, and/or supplemental data.

According to some embodiments, such as in the case that the candidatebuilding name is the same/similar to a known building but the addressesare different, this may be considered a “common” indicator that thebuildings are the same. The buildings in such a case could be the same,such as in the case that a single building with a single name issituated on a street corner and/or otherwise comprises addresses onmultiple streets.

In some embodiments, such as in the case that the candidate buildingname is the same/similar to a known building but the addresses aredifferent, this may be considered a “less common” indicator that thebuildings are different. The buildings in such a case could bedifferent, such as in the case that the name actually identifies an area(such as “State House Square”) comprising multiple buildings.

According to embodiments, such as in the case that the candidatebuilding name is different from a known building but the addresses arethe same/similar, this may be considered a “common” indicator that thebuildings are the same. The buildings in such a case could be the same,such as in the case that the names comprise variants and/or local,slang, and/or unofficial names. For example, in the case that the twonames are “Chem” and “Chemistry”—both descriptive of a particularchemistry building having a particular address.

In some embodiments, such as in the case that the candidate buildingname is different from a known building but the addresses are thesame/similar, this may be considered a “common” indicator that thebuildings are different. The buildings in such a case could bedifferent, such as in the case that the names comprise various companyand/or other informal designations, such as department names.

According to some embodiments, such as in the case that the candidatebuilding name is different from a known building and addresses aredifferent, this may be considered a “most common” indicator that thebuildings are different. The buildings in such a case are likelydifferent, for example, if the candidate is “Library” at “12 LearningLane” while the stored data is “Math Building” at “14 College Way”.

In some embodiments, such as in the case that the candidate buildingname is different from a known building and addresses are different,this may be considered a “least common” indicator that the buildings arethe same. The buildings in such a case may sometimes be the same, forexample, in the case that a particular building with different inputname variants straddles and/or spans addresses on two or more roads (orroad names). For example, if the candidate is “Chem” at “1 Oak St.” andthe stored info is “Chemistry” at “12 University Dr.” (or “Route 1”,where “Route 1” and “Oak St.” coincide), the buildings may be consideredthe same.

Rules sets, logic, and/or instructions, such as the example rule set342, may be utilized to compare location information other than or inaddition to the building name and address data described with respect toFIG. 3D. While the system 300 is depicted in FIG. 3A, FIG. 3B, FIG. 3C,and FIG. 3D as comprising the various example data storage structures340 a-c and other components 302, 310, 314 a-d, 316 a-c, 342, 344 a-c,fewer or more such components 302, 310, 314 a-d, 316 a-c, 340 a-c, 342,344 a-c may be included in the system 300 without deviating from thescope of some embodiments.

In some embodiments, fewer or more data fields than are shown may beassociated with the example data storage structures 340 a-c. Only aportion of one or more databases and/or other data stores is necessarilyshown in any of FIG. 3A, FIG. 3B, FIG. 3C, and/or FIG. 3D, for example,and other database fields, columns, structures, orientations,quantities, and/or configurations may be utilized without deviating fromthe scope of some embodiments. According to some embodiments, any or allof the components 302, 310, 314 a-d, 316 a-c, 340 a-c, 342, 344 a-c ofthe system 300 may be similar in configuration and/or functionality toany similarly named and/or numbered components described herein.

According to some embodiments, systems, methods, and articles ofmanufacture described herein may be utilized to gather location data(e.g., via the location data devices 102 a-n of FIG. 1 and/or withrespect to one or more locations, such as the example location 200 ofFIG. 2A, FIG. 2B, FIG. 2C, and/or FIG. 2D), form, identify, define,and/or otherwise determine relationships between the various locationdata (e.g., via the location processing device 110 of FIG. 1), and/orutilize such data (e.g., certified location and/or value subject data)to inform or facilitate various processes and/or perform various tasksas described herein.

Turning to FIG. 4, for example, a flowchart of a method 400 according tosome embodiments is shown. In some embodiments, the method 400 may beperformed and/or implemented by and/or otherwise associated with one ormore specialized and/or specially-programmed computers, computerterminals, computer servers, computer systems and/or networks, and/orany combinations thereof (e.g., by one or more third-party and/orinsurance company and/or underwriter computers, such as, e.g., thecertified location device 110 of FIG. 1). The functional diagrams andflow diagrams described herein do not necessarily imply a fixed order toany depicted actions, steps, and/or procedures, and embodiments maygenerally be performed in any order that is practicable unless otherwiseand specifically noted. Any of the processes and methods describedherein may be performed and/or facilitated by hardware, software(including microcode), firmware, or any combination thereof. Forexample, a storage medium (e.g., a hard disk, Universal Serial Bus (USB)mass storage device, a RAM device, a cache memory device, and/or DigitalVideo Disk (DVD)) may store thereon instructions that when executed by amachine (such as a computerized processor) result in performanceaccording to any one or more of the embodiments described herein.

In some embodiments, the method 400 may comprise improving locationdata, at 402. The improving may comprise, for example, receiving and/orretrieving location data. According to some embodiments, the improvingmay comprise gathering information descriptive of (i) street addressdata, (ii) land parcel data, (iii) structure data, and/or (iv)coordinate data. Various data devices (e.g., the location data devices102 a-n of FIG. 1) and/or sources (e.g., the database 140 of FIG. 1and/or the example data storage structures 340 a-c of FIG. 3A, FIG. 3B,and/or FIG. 3C) may be utilized, for example, to gather, acquire,assemble, and/or aggregate data regarding various locations. In someembodiments, the gathered data may be descriptive of and/or with respectto a plurality of customers. A business such as an insurance companymay, for example, gather and/or receive data descriptive of locations ofvarious customers and/or insured objects, places, areas, etc. Accordingto some embodiments, the location data may be gathered and/or receivedfrom any quantity, type, and/or combination of data sources that is orbecomes known or practicable.

In some embodiments, data may be retrieved and/or received from variousthird-party data sources. A third-party data vendor may provide streetaddress information based on a customer's telephone number, for example,or may utilize land parcel address block assignments to determine whichland parcel a customer resides on (and/or does business at). In someembodiments, location data may be manually and/or electronicallysourced, mined, scanned, copied, scraped, and/or otherwise obtained fromvarious municipal, public, private, and/or third-party sources. In someembodiments, the location data may be gathered utilizing one or moremobile devices. Customers, field agents, and/or third-party personnelmay, for example, utilize a mobile computing device with locationidentification capabilities (e.g., GPS, Bluetooth®, and/or cell-towertriangulation) to provide location data that is received by a centralcontroller and/or certified location system.

In some embodiments, the improving may comprise standardizing and/orde-duplicating location data received as input (e.g., from a customer)and/or validating the location data (e.g., a mailing address) againstknown and/or available location data (e.g., a list of known mailingaddress). In some embodiments, a confidence level and/or code may beestablished based on the quality and/or content of the location dataand/or upon the results of the verification process. In someembodiments, the location data may comprise a variety of informationincluding, but not limited to, street and/or mailing address data,vanity address data (e.g., informal place and/or street address name(e.g., “30 Rock”), area and/or attraction name data (e.g., MadisonSquare Garden), geospatial coordinates, an intersection identifier,suite number, floor or level number, utility information (e.g.,telephone pole number), and/or building and/or structure characteristicdata (e.g., brick exterior, flat roof, adjacent to town park).

According to some embodiments, the method 400 may comprise searching forexisting location records, at 404. Any or all location informationreceived, retrieved, and/or otherwise determined at 402, for example,may be utilized to query one or more databases and/or other data storesto determine whether (and/or a likelihood or probability of whether) thelocation data matches data already stored and/or available. In the casethat a match is determined, the existing database record may be utilizedand/or updated. In the case that no match is found, a new databaserecord may be created to store the location information. According tosome embodiments, such as in the case that a partial match is found, oneor more algorithms and/or procedures may be executed to determine aprobability associated with the match. Spelling and/or data entryvariations may be analyzed, for example, to determine a likelihood thatboth the incoming and previously stored/available matched locationinformation is descriptive of the same geographic point, polygon/valuesubject, parcel, building, etc.

In some embodiments, the method 400 may comprise gathering geospatialdata, at 406. Geospatial data descriptive of the location data (receivedat 402 and/or identified at 404) may, for example, comprise datadescriptive of various levels of geospatial detail. In some embodiments,the geospatial data may comprise detailed address information, polygonand/or polygon boundary information, parcel and/or parcel boundaryinformation, coordinate data, and/or structure or sub-structure data.According to some embodiments, the best available geospatial data may beidentified. In the case of an apartment in an apartment building, forexample, the mailing address, parcel identifier, structure identifier,floor number, security system zone, and/or geographic coordinate (and/orelevation) may be determined. In some embodiments, such as in the casethat the location information does not conflict/match with alreadystored information, any level of uniqueness (e.g., zip code, streetname, mailing address) may be utilized that is or becomes practicable ordesirable. It may be desirable, for example, to only search for (and/orpay for) and store enough geospatial data to uniquely identify alocation data record with respect to other stored location data records.

According to some embodiments, the method 400 may comprise identifyingassociations, at 408. The location data and the associated geospatialdata from 402, 404, and/or 406 may, for example, be compared to otherknown locations and/or location data records to determine one or morerelationships there between (e.g., utilizing stored rules, logic, and/orinstructions such as the example rule set 342 of FIG. 3D). In the casethat the geospatial location data comprises a definition of a polygondefining a land parcel, for example, other known polygons (e.g., landparcel and/or structure polygons) that intersect with the polygon of thelocation data and/or are otherwise proximate to the polygon of thelocation data, may be identified. In some embodiments, the relationshipmay comprise the polygon of the location data being disposed withinanother known polygon (partially or entirely; or vice versa; such as astructure polygon residing within the boundaries of a land parcelpolygon) and/or a point associated with the location data being disposedwithin a known polygon (e.g., a roof centroid of a structure beingdisposed within the polygon defining the boundaries of the structure).According to some embodiments, proximity and/or other relationships maybe analyzed to determine a nature and/or likelihood of relation. In thecase that a coordinate point of the location data falls near, but notwithin, a parcel and/or structure polygon, for example, one or moreroutines and/or procedures may be executed to determine a probabilitythat the point is associated with the nearby parcel/structure (and/or todetermine which nearby parcel and/or structure the point is likelyassociated with). In the case that two polygons descriptive of the sametype of object (e.g., the same level of geospatial data), such as twoland parcel polygons, overlap and/or overlap by some thresholdpercentage or amount (e.g., fifty percent (50%) overlap), a qualitycontrol check may be initiated to determine why such overlap exists.According to some embodiments, analysis of conflicting location data maylead to data modification decisions and/or the location and/orgeospatial data may otherwise be modified (e.g., new and/or betterlocation and/or geospatial data may be located based on an analysis).

In some embodiments, the method 400 may comprise disambiguating thelocation data, at 410. In the case that one or more associations areidentified at 408, for example, information distinguishing associatedand/or conflicting location data may be determined. In the case that twobuildings are identified at a single address (or more than one buildingis possible based on the location data from 402), for example,distinguishing information such as coordinate data, structure type, yearbuilt, number of stories, size, etc., may be utilized to separatelyidentify the two structures. In some embodiments, one or more storedrules, logic processes, instructions, and/or programs may be utilized toanalyze, interpret, interpolate, make educated assumptions regarding,and/or otherwise process received and stored data via one or more datacomparisons (e.g., utilizing stored rules, logic, and/or instructionssuch as the example rule set 342 of FIG. 3D).

According to some embodiments, the method 400 may comprise assigning aunique identifier, at 412. The various overlapping and/or conflictingstructures from the example at 410, for example, may each be assigned aunique identifier—e.g., a “certified location” identifier, such as thecertified location certificate number 344 b-2 of FIG. 3B and/or FIG. 3Cand/or the location ID field 344 a-1 of FIG. 3A. In some embodiments,such as in the case that multiple apartments or even rooms in a buildingare disambiguated (e.g., at 410), the unique certified locationidentifier may be assigned (and/or may be unique) for each such room,apartment, etc. According to some embodiments, identifiers (e.g., uniqueidentifiers) may be stored for every distinguishable polygon and/orpoint representing various geographic locations (e.g., for every valuesubject). In some embodiments, the assigning may comprise storing (e.g.,in a database device, such as the database 140 of FIG. 1 and/or in adata storage structure such as one or more of the example data storagestructures 340 a-c of FIG. 3A, FIG. 3B, and/or FIG. 3C) informationdefining data storage relationships between (a) the street address dataand the land parcel data, (b) the land parcel data and the structuredata, and one or more of (c) the land parcel data and the coordinatedata, and (d) the structure data and the coordinate data. In someembodiments, relationships may be automatically and/or electronicallyconstructed based on the underlying location data. Land parcel data maybe stored with indication of which street addresses correspond to aparticular land parcel, for example, and/or street addresses may bestored with indications of which land parcel(s) corresponds to aparticular street address. Similarly, land parcel data may be storedwith indication of which structures are located on (or at leastpartially on) a particular land parcel, and/or structure data may bestored with indications of which land parcels are associated with aparticular structure. In some embodiments, coordinates associated with aparticular structure and/or land parcel may be stored in relation to theparticular structure and/or land parcel. Various vertex, centroid, edge,border, and/or other coordinate points (e.g., the vertices 212 and/orthe points 214 of FIG. 2A and/or FIG. 2C) descriptive of a structureand/or parcel may, for example, be stored in a database recordassociated with the given structure and/or parcel.

In some embodiments, the method 400 may comprise determining locationrelationships, at 414. The determining may comprise, for example,determining (e.g., based on the defined data storage relationshipsand/or the unique identifier(s) assigned at 412) which of a plurality ofobjects (e.g., customers, structures, value subjects, rooms, apartments)are at least one of: (1) located on the same land parcel, and (2)located in the same building. According to some embodiments, thelocation data may be stored in such a manner that information definingany pertinent aspect of a location may be utilized to determine anyother pertinent aspect of the location. For example, in the case that astreet address is known or provided, any or all of a land parcel andstructure for that particular location (e.g., a certified location/valuesubject) may be identified utilizing the stored relationships (e.g.,stored and/or defined at 404). In some embodiments, the relationshipsand/or the determining may comprise utilization of the coordinateinformation. In the case that a coordinate of a street address and/orcustomer location is known and/or provided (e.g., via a mobile computingdevice), for example, the coordinate information stored with respect tothe land parcels and/or structures may be queried to determine whichland parcel(s) and/or which structure(s) correspond to the providedcoordinate. In the case that a range of coordinates (and/or elevationdata) are stored with respect to a particular structure, such as vertexcoordinates for example, a query coordinate may be compared to thestructure coordinates (e.g., via a calculation, by analyzing the rangeof structure coordinates, by analyzing a matrix of structurecoordinates, and/or any combination thereof) to determine if the querycoordinate is descriptive of the structure. In some embodiments, such asin the case that a provided and/or available coordinate is utilized toidentify a customer, parcel, structure, and/or address, but does notfall within the boundaries of the parcel and/or structure (e.g., thecoordinate falls in or on a nearby street or sidewalk), the determiningmay comprise analyzing the proximity of the coordinate to othercoordinates stored in association with nearby parcels and/or structures(e.g., to determine which parcel and/or structure is most likely to berepresented by the coordinate). In some embodiments, one or more storedrules, logic processes, instructions, and/or programs may be utilized todetermine the location relationships utilizing various availablelocation data (e.g., utilizing stored rules, logic, and/or instructions,such as the example rule set 342 of FIG. 3D).

In such a manner, for example, it may be determined which customers(and/or other individuals or entities) share common spaces such as landparcels and/or structures. As described herein, commonality of parceland/or structure association between customers may be utilized toinform, facilitate, and/or conduct various business processes. In oneexample utilized throughout herein, an insurance company may utilizeknowledge of common customer attributes (e.g., mailing address, parcelID, structure ID, zip code, floor, and/or elevation) to determine (i)how many customers exist in the same building (or on the same parcel ofland), (ii) how much insurance risk has been underwritten for a givenstructure, parcel, and/or other value subject, (iii) whether anunderwriting product for a particular structure/parcel/value subjectshould be written (e.g., based on current exposure and/or risk), (iv)claim/loss data for a given structure/parcel/value subject, and/or (v)one or more prospective customers (e.g., in relation to one or morecustomers already underwritten in a particular building and/or on aparticular parcel). In some embodiments, business decisions, such asunderwriting, pricing, risk, and/or claim/loss decisions, may be basedon a plurality of identifiable structures, land parcels, and/or othervalue subjects (e.g., based on certified location and/or value subjectdata) that are of particular interest—e.g., a group of buildings may beanalyzed together and/or a city block may be analyzed as a whole (i.e.,a particular value subject). In some embodiments, stored relationshipsutilized to determine certified locations may be determined withreference to one or more particular database and/or data storagestructures via which such stored relationships are defined.

Referring to FIG. 5A and FIG. 5B, for example, diagrams of an exampledata storage structure 540 according to some embodiments are shown. Insome embodiments, the data storage structure 540 may comprise aplurality of data tables such as a customer table 540 a, a polygon table540 b, a structure table 540 c, a coordinate table 540 d, a structurehistory table 540 e, and/or a sub-structure table 540 f. The data tables540 a-f may, for example, be utilized (e.g., at 404 of the method 400 ofFIG. 4) to store location, value subject, and/or certified locationinformation.

The customer table 540 a may comprise, in accordance with someembodiments, a customer IDentifier (ID) field 544 a-1, an apartmentnumber field 544 a-2, a suite number field 544 a-3, a street numberfield 544 a-4, a street name field 544 a-5, a city field 544 a-6, astate field 544 a-7, a zip code field 544 a-8, a zip+4 field 544 a-9, apolygon ID field 544 a-10, a structure ID field 544 a-11, and/or asub-structure ID field 544 a-12. Any or all of the ID fields 544 a-1,544 a-10, 544 a-11, 544 a-12 may generally store any type of identifierthat is or becomes desirable or practicable (e.g., a unique identifier,an alphanumeric identifier, and/or an encoded identifier). As an exampleof how the example data structure 540 may be utilized in accordance withsome embodiments, the first and third records in the customer table 540a (i.e., customers “1234-5678” and “CHRIS3482”) may correspond to thesecond and fourth customer locations 218-2, 218-4 of the examplelocation 200 of FIG. 2A and FIG. 2B herein. It can be readily determinedfrom the data in the customer table 540 a that the two customers havedifferent street/mailing addresses, albeit in the same city. Anadvantage of the example data structure 540 is that by utilizing thepolygon ID field 544 a-10, structure ID field 544 a-11, and/orsub-structure ID field 544 a-12, it may be readily determined that eventhough the addresses of the customers are different, they reside and/ordo business in the same building (and on the same parcel of land).

The polygon table 540 b may comprise, in accordance with someembodiments, a polygon ID field 544 b-1, a parcel type field 544 b-2, acentroid coordinate ID field 544 b-3, a first vertex coordinate ID field544 b-4, and/or an ‘n^(th)’ vertex coordinate ID field 544 b-n. Thestructure table 540 c may comprise, in accordance with some embodiments,a structure ID field 544 c-1, a polygon ID field 544 c-2, a structuretype field 544 c-3, a sub-structure flag field 544 c-4, a year builtfield 544 c-5, a year renovated field 544 c-6, a centroid coordinate IDfield 544 c-7, a first corner coordinate ID field 544 c-8, and/or an‘n^(th)’ corner coordinate ID field 544 c-n. In some embodiments, suchas in the case that different data for the year built field 544 c-5 isgathered from different sources (e.g., different customers) with respectto the same structure, the data may be analyzed to determine variousattributes of the year the structure was built such as, but not limitedto: which year built data is likely to be correct, what the average yearbuilt is based on stored data, and/or whether the stored data isindicative of any events such as renovations (e.g., that has incorrectlybeen stored as an indication of what year the structure was erected). Insome embodiments, the coordinate fields 544 c-7, 544 c-8, 544 c-n mayalso or alternatively comprise a single field storing a representative(e.g., “best” available point) and/or aggregate point and/or coordinatevalue. However the point/coordinate information is stored in the polygontable 540 b, the information may generally represent and/or define oneor more polygons associated with and/or defining one or more parcels.

The coordinate table 540 d may comprise, in accordance with someembodiments, a coordinate ID field 544 d-1, a coordinate type field 544d-2, a confidence level field 544 d-3, a latitude field 544 d-4, alongitude field 544 d-5, and/or an elevation field 544 d-6. Thestructure history table 540 e may comprise, in accordance with someembodiments, a structure ID field 544 e-1, an event type field 544 e-2,an event date field 544 e-3, and/or a loss amount field 544 e-4. Thesub-structure table 540 f may comprise, in accordance with someembodiments, a structure ID field 544 f-1, a sub-structure ID field 544f-2, a sub-structure type field 544 f-3, a sub-structure first cornercoordinate ID field 544 f-4, and/or a sub-structure ‘n^(th)’ cornercoordinate ID field 544 f-n.

In some embodiments, certified locations and/or value subjects may bedefined by relationships established between two or more of the datatables 540 a-f. As depicted in the example data storage structure 540,for example, a first relationship “A” may be established between thecustomer table 540 a and the polygon table 540 b. In some embodiments(e.g., as depicted in FIG. 5A), the first relationship “A” may bedefined by utilizing the polygon ID field 544 a-10 as a data key linkingto the polygon ID field 544 b-1. According to some embodiments, thefirst relationship “A” may comprise any type of data relationship thatis or becomes desirable, such as a one-to-many, many-to-many, ormany-to-one relationship. In the case that multiple customers are likelyto reside and/or do business at a particular polygon (e.g., a particularland parcel and/or value subject), the first relationship “A” maycomprise a many-to-one relationship (e.g., many customers per singlepolygon/parcel—such as the two customer records in the customer table540 a that are depicted as linking to the polygon table 540 b via thefirst relationship “A”). In such a manner, for example, a customerand/or the customer's address may be associated and/or linked with oneor more underlying polygons/land parcels/value subjects. While theexample first relationship “A” represents a link and/or relationshipbetween one or more customer and one or more certified locations and/orvalue subjects, in accordance with some embodiments, customerinformation and/or relationships may not be necessary and/or desired.Certified location and/or value subject information may be utilizedwithout customer information, for example, to determine, plan, inform,and/or facilitate various processes such as may be implemented byvarious types of organizations (e.g., utility companies, municipalities,banking institutions, etc.)—e.g., by purchasing access to such dataaccessible via an interface provided in accordance with some embodimentsherein (such as the interfaces 620, 920, 1520 of FIG. 6, FIG. 9, and/orFIG. 15 herein).

According to some embodiments, a second relationship “B” may beestablished between the customer table 540 a and the structure table 540c. In some embodiments (e.g., as depicted in FIG. 5A), the secondrelationship “B” may be defined by utilizing the structure ID field 544a-11 as a data key linking to the structure ID field 544 c-1. Accordingto some embodiments, the second relationship “B” may comprise any typeof data relationship that is or becomes desirable, such as aone-to-many, many-to-many, or many-to-one relationship. In the case thatmultiple customers are likely to reside and/or do business in aparticular structure/building/value subject, the second relationship “B”may comprise a many-to-one relationship (e.g., many customers per singlestructure/value subject—such as the two customer records in the customertable 540 a that are depicted as linking to the structure table 540 cvia the second relationship “B”). In such a manner, for example, acustomer and/or the customer's address may be associated and/or linkedwith one or more particular structures/value subjects.

Utilizing the first and second relationships, “A” and “B”, it mayaccordingly be possible to readily identify for any particular customerand/or address (or other provided location information such as points,lines, and/or polygons) one or more specific land parcels, valuesubjects, and/or one or more specific buildings associated therewith. Insuch a manner, for example, the identity and/or number of customersresiding at a particular parcel/value subject/building may be determined(e.g., at 406 of the method 400 of FIG. 4). In some embodiments, such asin the case that the structure table 540 c comprises the polygon IDfield 544 c-2, a third relationship “C” may also or alternatively beutilized to link particular structures to particular polygons/valuesubjects/land parcels. In some embodiments, the third relationship “Cmay be utilized in addition to or in place of the second relationship“B”.

In some embodiments, a fourth relationship “D” may be establishedbetween the polygon table 540 b and the coordinate table 540 d (depictedas linking between FIG. 5A and FIG. 5B via the numeral “1”). In someembodiments (e.g., as depicted in FIG. 5A and FIG. 5B), the fourthrelationship “D” may be defined by utilizing any or all of the variouscoordinate ID fields 544 b-3, 544 b-4, 544 b-n as a data key linking tothe coordinate ID field 544 d-1. According to some embodiments, thefourth relationship “D” may comprise any type of data relationship thatis or becomes desirable, such as a one-to-many, many-to-many, ormany-to-one relationship. In the case that polygons/valuesubjects/parcels are likely to have unique centroids, the fourthrelationship “D” utilizing the centroid coordinate ID 544 b-3 maycomprise a one-to-one relationship. In the case that multiplepolygons/value subjects/parcels are likely to share coordinates (e.g.,adjoining polygons/value subjects/land parcels such as the first andsecond polygons 210 a-b of FIG. 2A, FIG. 2B, FIG. 2C, and/or FIG. 2D),the fourth relationship “D” may comprise a many-to-one relationship. Insuch a manner, for example, a polygon/value subject/parcel may beassociated and/or linked with one or more coordinates. As described inaccordance with some embodiments herein, the coordinates associated witha polygon/value subject/land parcel may be utilized to establish and/orverify relationships between the polygon/value subject/parcel and one ormore customers, addresses, points/coordinates, and/or structures (and/orsub-structures), and/or any combinations thereof.

In some embodiments, a fifth relationship “E” may be established betweenthe structure table 540 c and the coordinate table 540 d (depicted aslinking between FIG. 5A and FIG. 5B via a second depiction of thenumeral “1”). In some embodiments (e.g., as depicted in FIG. 5A and FIG.5B), the fifth relationship “E” may be defined by utilizing any or allof the various coordinate ID fields 544 c-7, 544 c-8, 544 c-n as a datakey linking to the coordinate ID field 544 d-1. According to someembodiments, the fifth relationship “E” may comprise any type of datarelationship that is or becomes desirable, such as a one-to-many,many-to-many, or many-to-one relationship. In the case that structuresare likely to have unique centroids, the fifth relationship “E”utilizing the centroid coordinate ID 544 c-7 may comprise a one-to-onerelationship. In the case that multiple structures are likely to sharecoordinates (e.g., adjoining structures such as the second and thirdstructures 216 a-2, 216 b of FIG. 2A, FIG. 2B, FIG. 2C, and/or FIG. 2D),the fifth relationship “E” may comprise a many-to-one relationship. Insuch a manner, for example, a structure may be associated and/or linkedwith one or more coordinates. As described in accordance with someembodiments herein, the coordinates associated with a structure may beutilized to establish and/or verify relationships between the structureand one or more customers, addresses, polygons, value subjects, parcels,and/or sub-structures.

According to some embodiments, a sixth relationship “F” may beestablished between the structure table 540 c and the structure historytable 540 e (depicted as linking between FIG. 5A and FIG. 5B via thenumeral “2”). In some embodiments (e.g., as depicted in FIG. 5A and FIG.5B), the sixth relationship “F” may be defined by utilizing thestructure ID field 544 c-1 as a data key linking to the structure IDfield 544 e-1. According to some embodiments, the sixth relationship “F”may comprise any type of data relationship that is or becomes desirable,such as a one-to-many, many-to-many, or many-to-one relationship. In thecase that a structure is likely to have multiple historical eventsassociated therewith, the sixth relationship “F” may comprise aone-to-many relationship. In the case that multiple structures arelikely to share certain historical events (e.g., adjoining structuressuch as the second and third structures 216 a-2, 216 b of FIG. 2A, FIG.2B, FIG. 2C, and/or FIG. 2D may be affected by a single fire,earthquake, etc.), the sixth relationship “F” may comprise amany-to-many relationship. In such a manner, for example, a structuremay be associated and/or linked with various historical events that maybe readily determined via the sixth relationship “F”.

In some embodiments, a seventh relationship “G” may be establishedbetween the structure table 540 c and the sub-structure table 540 f(depicted as linking between FIG. 5A and FIG. 5B via the numeral “3”).In some embodiments (e.g., as depicted in FIG. 5A and FIG. 5B), theseventh relationship “G” may be defined by utilizing the structure IDfield 544 c-1 as a data key linking to the structure ID field 544 f-1.According to some embodiments, the seventh relationship “G” may compriseany type of data relationship that is or becomes desirable, such as aone-to-many, many-to-many, or many-to-one relationship. In the case thata structure is likely to have multiple sub-structures (but asub-structure is likely to exist only within a single particularstructure), the seventh relationship “G” may comprise a one-to-manyrelationship. In such a manner, for example, a structure may beassociated and/or linked with various sub-structures that may be readilydetermined via the seventh relationship “G”.

According to some embodiments, an eighth relationship “H” may beestablished between the sub-structure table 540 f and the coordinatetable 540 d. In some embodiments (e.g., as depicted in FIG. 5B), theeighth relationship “H” may be defined by utilizing the coordinate IDfield 544 d-1 as a data key linking to either or both of thesub-structure first corner coordinate ID field 544 f-4 and thesub-structure ‘n^(th)’ corner coordinate ID field 544 f-n. According tosome embodiments, the eighth relationship “H” may comprise any type ofdata relationship that is or becomes desirable, such as a one-to-many,many-to-many, or many-to-one relationship. In the case that asub-structure is likely to have multiple associated coordinates, theeighth relationship “H” may comprise a many-to-one relationship. In sucha manner, for example, a sub-structure may be associated and/or linkedwith various coordinates that may be readily determined via the eighthrelationship “H”. In some embodiments, a sub-structure may comprise avalue subject—e.g., a particular fire-segmented portion of a building.

In some embodiments, fewer or more data fields than are shown may beassociated with the data tables 540 a-f. Only a portion of one or moredatabases and/or other data stores is necessarily shown in any of FIG.5A and/or FIG. 5B, for example, and other database fields, columns,structures, orientations, quantities, and/or configurations may beutilized without deviating from the scope of some embodiments. Accordingto some embodiments, such as in the case that addresses are desired tobe linked to polygons, parcels, and/or structures while retainingflexibility to associate different customers with various addresses, aseparate address table (not shown) may be utilized. Further, the datashown in the various data fields is provided solely for exemplary andillustrative purposes and does not limit the scope of embodimentsdescribed herein.

Referring now to FIG. 6, a block diagram of a system 600 according tosome embodiments is shown. In some embodiments, the system 600 maycomprise a plurality of network devices 602 a-b (such as data sourcedevices 602 a and/or user devices 602 b), a server 610 (comprising acertified locations service layer 610-1, an enterprise services layer610-2, and/or an interface 620), a plurality of databases 640 a-d, anExtract Transform and Load (ETL) service 660, and/or a geo-server 662.According to some embodiments, any or all of the components 602 a-b,610, 620, 640 a-d, 660, 662 of the system 600 may be similar inconfiguration and/or functionality to any similarly named and/ornumbered components described herein. Fewer or more components 602 a-b,610, 620, 640 a-d, 660, 662 and/or various configurations of thecomponents 602 a-b, 610, 620, 640 a-d, 660, 662 may be included in thesystem 600 without deviating from the scope of embodiments describedherein. While multiples of some components 602 a-b, 640 a-d are depictedand while single instances of other components 610, 620, 660, 662 aredepicted, for example, any component 602 a-b, 610, 620, 640 a-d, 660,662 depicted in the system 600 may comprise a single device, acombination of devices and/or components 602 a-b, 610, 620, 640 a-d,660, 662, and/or a plurality of devices, as is or becomes desirableand/or practicable.

In some embodiments, the data source devices 602 a may comprise one ormore of a map data source 602 a-1, a hosted map layers data source 602a-2, and/or an address scrubbing/geocoding data source 602 a-3 whichmay, for example, be hosted by the certified locations service layer610-1 (and/or the server 610). The certified locations service layer610-1 may, for example, provide access, via the enterprise service layer610-2 and/or interface 620, to one or more of the user devices 602 bsuch as a customer device 602 b-1, a risk control device 602 b-2, aclaims device 602 b-3, and/or a certified location data steward device602 b-4. According to some embodiments, the certified locations servicelayer 610-1 (and/or the interface 620) may provide access to one or moreof the databases 640 a-d. The certified locations service layer 610-1(and/or interface 620) may, for example, provide access to a back-enddatabase 640 a, a transactional database 640 b, a data store 640 c,and/or a data warehouse 640 d. In some embodiments, access to thevarious data sources 602 a and/or databases 640 a-d may be limitedand/or managed. The customer device 602 b-1, risk control device 602b-2, and/or claims device 602 b-3 may, for example, be permitted toquery any or all of the various data sources 602 a and/or databases 640a-d, but may only be able to write to (e.g., modify) the transactionaldatabase 640 b. Certain devices (and/or users) such as the certifiedlocation data steward device 602 b-4 may have expanded access to thesystem 600 and may be able to access the certified locations servicelayer 610-1 directly (e.g., instead of or in addition to having accessvia the enterprise services layer 610-2 and/or the interface 620) and/ormay have write/edit access directly to the data warehouse 640 d.

According to some embodiments, data descriptive of customer (and/orvalue subject) locations may be retrieved from the data sources 602 aand/or may be entered via one or more of the network devices 602 b. Aninsurance agent or data customer (and/or underwriter, actuary, and/oroperations personnel) operating the customer device 602 b-1 may, forexample, enter and/or retrieve data descriptive of a location of acustomer (and/or a location associated with an object desired to beinsured by the customer—e.g., a value subject). In some embodiments, thelocation data may be stored in the transactional database 640 b. In someembodiments, other data such as insurance policy data, coverage data,and/or underwriting rules or questions may be stored in the data store640 c. In such a manner, for example, the insurance agent or datacustomer (and/or Customer Service Representative (CSR) and/orunderwriter, etc.) may access, retrieve, and/or define customer and/orvalue subject location information and utilize the system 600 to assessrisk for, quote, and/or sell an underwriting and/or other type ofproduct to the customer.

In some embodiments, the transactional database 640 b may be interfacedwith the data warehouse 640 d on an intermittent basis (e.g., nightly)such as by utilizing the ETL service 660 to update the data warehouse640 d in a secure and controlled manner (e.g., based on the day'stransactions stored in the transactional database 640 b). According tosome embodiments, the geo-server 662 may be utilized to translate dataand/or provide access to the data warehouse 640 d to a network device602 b such as an analytics device 602 b-5. The analytics device 602 b-5may comprise a device configured to execute data mining, reporting, datavisualization, and/or other data analysis tools. The analytics device602 b-5 may be utilized, for example, to determine relationships betweencustomers, certified locations, value subjects, buildings, parcels,and/or to determine business-specific information such as (in thecontext of an insurance business) total realized losses for a particularparcel or structure, total risk or exposure for a particular parcel,value subject, and/or structure, to conduct data modeling, and/or tofacilitate and/or conduct customer prospecting.

According to some embodiments, the system 600 may be utilized todetermine and/or utilize certified location and/or value subject data asdescribed in accordance with embodiments presented herein. In someembodiments, the system 600 may be utilized to dynamically determineand/or populate certified location and/or value subject data based oncustomer transactions (e.g., conducted by the customer device 602 b-1).Incoming data may be compared to existing data to determine, forexample, if data descriptive of a location already exists (e.g., in thetransactional database 640 b and/or the data warehouse 640 d) orconflicts with previously stored information—e.g., a new customerprovides address, point, line, and/or polygon information that isidentical to a different and existing customer. In some embodiments,such comparisons and/or conflict determinations may be utilized todynamically increase data granularity/specificity defining one or morecertified locations and/or value subjects. In some embodiments, in thecase that a location has already been certified, the certified locationinformation may be utilized to reference other previously developed,stored, and/or purchased data.

Turning to FIG. 7, for example, a flowchart of a method 700 according tosome embodiments is shown. According to some embodiments, the method 700may be implemented, facilitated, and/or performed by or otherwiseassociated with any of the systems 100, 600 of FIG. 1 and/or FIG. 6herein. In some embodiments, the method 700 may be performed and/orimplemented by and/or otherwise associated with one or more specializedand/or specially-programmed computers, computer terminals, computerservers, computer systems and/or networks, and/or any combinationsthereof (e.g., by one or more third-party and/or insurance companyand/or underwriter computers, such as, e.g., the location processingdevice 110 of FIG. 1).

According to some embodiments, the method 700 may comprise receiving anindication of first location information descriptive of a first locationassociated with a first customer, at 702. A customer seeking aninsurance product may, for example, provide location and/or identifyinginformation such as via a website interface and/or via a device operatedby an insurance agent and/or customer service representative. In someembodiments, customer location information may be received from one ormore mobile devices such as by receiving coordinate information from amobile device operated by the customer, a field agent, and/or otherpersonnel or entities at the desired customer location. According tosome embodiments, information received from the customer may be utilizedto query one or more data stores and/or data sources to determinelocation information of the customer. The first location information maybe received from a variety of devices and/or sources such as thelocation data devices 102 a-n of FIG. 1, the data source devices 602 aand/or the customer device 602 b-1, both of FIG. 6.

In some embodiments, the method 700 may comprise determining that thefirst location information is also descriptive of a second locationassociated with a second customer, at 704. A database such as thedatabase 140 of FIG. 1 and/or the transactional database 640 b or datawarehouse 640 d of FIG. 6 may, for example, be queried utilizing thefirst location information. Information corresponding to the firstlocation information may accordingly be identified if already stored(e.g., with respect to a second location and/or second customer). Insome embodiments, the descriptiveness may comprise a relationship of acertain degree, magnitude, and/or weight. In the case that a streetaddress for the customer matches a street address for a customer recordalready stored in a database, for example, a first degree of matchingmay be determined. A second degree of matching may comprise, forexample, a correspondence between a coordinate, parcel identifier,and/or structure identifier of the customers/locations. In someembodiments, such as in the case that the customer's name or othernon-location information does not match that stored with respect to theidentified matching location record, an initial presumption ofconflicting location information may be identified (e.g., an initialpresumption that the first and second customers are differentindividuals and/or entities). In the case that the customer name matchesthe one on record for the corresponding location information, adetermination may be made that the customer is an existing customer(e.g., the first and second customers are the same) and/or that thefirst and second locations are the same.

According to some embodiments, the method 700 may comprise requesting(e.g., in response to the determining that the first locationinformation is also descriptive of the second location associated withthe second customer) information that distinguishes the first and secondlocations, at 706. In the case that identical information is alreadystored, an alert, message, and/or trigger may be initiated. An insuranceagent (or the customer, if the customer is self-quoting a policy online)may be presented with a pop-up message and/or prompt, for example,requesting that the agent (and/or customer) verify that the firstcustomer and/or the first customer's location is different than thesecond customer and/or the second customer's location. In someembodiments, if it is determined that the first and second customersand/or locations are different, the distinguishing information may berequested and/or actively sought (e.g., by querying data sources and/orby dispatching a field agent request for more detailed information).According to some embodiments, the distinguishing information requestedand/or sought may comprise a specific type and/or piece of information.In the case that both customers' street address match, for example,structure information (e.g., suite, apartment number, floor, elevation,building color, facade type, etc.) may be requested/sought to determineif the customers are located within the same building.

In some embodiments, the method 700 may comprise receiving (e.g., inresponse to the requesting) third location information thatdistinguishes the first and second locations, at 708. The insuranceagent, customer (first or second customer), an underwriter, and/or athird-party data source or supplier may, for example, provide the thirdlocation information to a central system (e.g., via a website,workstation, and/or mobile device). In some embodiments, such as in thecase that the first customer and/or agent are utilizing a mobile deviceat the first location, the requesting at 706 and the receiving at 708may be conducted automatically and/or without input from the customerand/or agent. An application running on the mobile device mayautomatically interface with a central system, for example, to providenecessary third location information such as coordinate, elevation,directional, and/or other information. In some embodiments, the customerand/or agent may send (and a central system may receive) photographs,pictures, and/or images of the location. The photos may be utilized todistinguish the first and second locations such as in the case thatphotos and/or descriptions of the second location are already stored andcan be readily compared to the newly-received photos to identifydifferences between the locations. In some embodiments, image processingmay be conducted to electronically distinguish the locations based onreceived photos (and/or other received distinguishing data).

According to some embodiments, the method 700 may comprise creating adata storage relationship between the third location information and atleast one of the first and second locations, at 710. In the case thatthe third location information is descriptive of the first location(e.g., and is received from the first customer and/or an agent workingwith the first customer), for example, the third location informationmay be stored in a transactional database (e.g., the transactionaldatabase 640 b of FIG. 6) of an insurance company system to distinguishthe first and second locations. In such a manner, for example, lessdetailed levels of location information may be required in a processingsystem until a conflict or question is identified regarding thedescribed location. For example, in the case that an existing customerhas provided a street name and number defining address information for alocation, no further detail or granularity of data may be required to beentered into a system. Upon entry of a new customer into the system,however, the newly entered information may be compared to the existinginformation to identify any potential conflicts. If a conflict or matchexists and it is determined that the new customer is not the same as theexisting customer, the granularity of stored data for either or both ofthe locations/customers may be required to be increased. Apartmentnumbers, structure identifiers and/or attributes, suite numbers, floornumbers, directional attributes (e.g., North-side, west wing), and/orcoordinates may, for example, be required to uniquely identify thecustomers. As some or all of these more detailed levels of locationinformation may be more time-consuming, expensive, and/or difficult toobtain, it may be desirable to only require their entry in the case ofan identified conflict—thus reducing costs associated with a certifiedlocation system in accordance with embodiments described herein.

Referring now to FIG. 8, a flowchart of a method 800 according to someembodiments is shown. According to some embodiments, the method 800 maybe implemented, facilitated, and/or performed by or otherwise associatedwith any of the systems 100, 600 of FIG. 1 and/or FIG. 6 herein. In someembodiments, the method 800 may be performed and/or implemented byand/or otherwise associated with one or more specialized and/orspecially-programmed computers, computer terminals, computer servers,computer systems and/or networks, and/or any combinations thereof (e.g.,by one or more third-party and/or insurance company and/or underwritercomputers, such as, e.g., the location processing device 110 of FIG. 1).In some embodiments, the method 800 may be executed, conducted, and/orfacilitated by and/or via an interface, such as the interfaces 620, 920,1520 of FIG. 6, FIG. 9, and/or FIG. 15 herein.

According to some embodiments, the method 800 may comprise providing(e.g., by a processing device and/or via a GUI and/or an electroniccommunications network) an interface, at 802. The interface may, forexample, comprise a graphical depiction of a geographical area, such asa map interface. In some embodiments, the interface may be provided viaa website and/or mobile device application and/or other softwareprogram. The interface may, in some embodiments, automatically display agraphical depiction of a geographical area in which a user (and/or userdevice) is located. According to some embodiments, the interface maydisplay representations of various certified location data, such asparcels, structures, buildings, etc.

In some embodiments, the method 800 may comprise receiving (e.g., by theprocessing device and/or via the GUI and/or the electroniccommunications network) an indication of a user-defined location, at804. The user-defined location may comprise, for example, a point, line,and/or polygon input by the user via a provided interface (e.g., theinterface provided at 802). In some embodiments, the user input maycomprise touch screen, mouse, other pointer, and/or other drawing inputthat designates one or more points and/or pixels as desired locationindicators. According to some embodiments, the user input may comprise aplurality of points, lines, line segments, shapes, and/or other drawinginput that defines an area (or areas) of interest for defining a valuesubject and/or for acquiring value subject (or other) data. In someembodiments, the user input may be provided via a user and/or networkdevice to an application, web page, and/or server (e.g., via theinterface). The user input may comprise, for example, input via theinterface that is provided on (e.g., on top of, over, and/or as a layerof) the display of the graphical depiction of a geographical area, suchthat the input, for example, indicates one or more real-world locations(e.g., geo-spatial points and/or areas). In some embodiments, theindication may be received from a third-party. In some embodiments, theuser input may comprise an indication of a particular elevation and/ordata layer.

According to some embodiments, the method 800 may comprise determining(e.g., by the processing device and/or via the GUI and/or the electroniccommunications network) a value subject, at 806. Based on the receiveduser input and/or user-defined location data, for example, one or morerelevant value objects may be identified, defined, and/or otherwisedetermined. In some embodiments, the user input may be utilized for oneor more of two different (but related) purposes. First, for example, theuser input (e.g., the user-defined location and/or indication thereof)may be utilized to identify any value subjects for which data mayalready be stored and/or which themselves are already defined. In thecase that a user indicates a point that is within a predeterminedthreshold distance from a known value subject (and/or point, line,boundary, etc., thereof), for example, that value subject (e.g., theclosest known value subject to the location identified by the user) maybe identified. In some embodiments, logic, rules, and/or proceduressimilar to those described herein with respect to resolving whether userinput matches and/or indicates a particular certified location may beutilized to determine whether a known value subject is reasonablyindicated by the user input and/or to identify which of a plurality ofknown value subjects is most likely being indicated by the user.According to some embodiments, a user may expressly indicate a selectionof a particular pre-defined value subject such as by clicking (orotherwise selecting) an interface element that displays a graphicalrepresentation of the value subject. In some embodiments, such as in thecase that the user input comprises elevation and/or layer data orindications, a value subject may be determined by interrogating athree-dimensional model and/or data store, such as a digital elevationmodel. A first value subject at a particular certified location may beassociated with or correspond to a first elevation at the location, forexample, while a second (e.g., different) value subject at theparticular certified location may be associated with or correspond to asecond (e.g., different) elevation at the location.

Second, for example, the user input (e.g., the user-defined locationand/or indication thereof) may be utilized to define one or more valuesubjects. In the case that the user input is known or determined not tobe indicative of any known value subject, for example, a new valuesubject may be defined based on the user input. The user input maycomprise a drawing of a boundary and/or shape such as a polygon on aprovided GUI, for example, and the bounded area may define theboundaries of a new value subject. According to some embodiments, thedefinition of value subject points, lines, boundaries, and/or areas(e.g., polygons) may be conducted in accordance with one or more rulesand/or preference settings. User-drawn lines or points, for example, maybe “snapped” to a grid, coordinate grid, and/or location features orattributes such as building corners, lot lines, parcel boundaries, floodplain boundaries, roadway boundaries, etc. According to someembodiments, the value subject defined (or identified) by the user inputmay comprise a provisional, draft, proposed, and/or initial (e.g.,first) value subject (e.g., later to be modified, altered, and/oradjusted).

In some embodiments, the method 800 may comprise determining (e.g., bythe processing device and/or via the GUI and/or the electroniccommunications network) value subject data, at 808. In the case that theuser-defined location data is utilized to identify or select one or moreknown value subjects at 806, for example, data stored in associationwith the known value subject may be retrieved from data storage. In someembodiments, such retrieval may require data transformation and/ormanipulation. The value subject data may be stored, for example, in oneor more databases, such as a third-party database, in a particularformat. Typical stored location data, for example, requires a streetaddress as a key and/or query term to locate appropriate correspondingdata. According to some embodiments, such as in the case that theuser-defined location data is not presented in the same form or formatrequired for querying the database (e.g., a vendor database), theuser-defined data may be transformed and/or mapped to an appropriateformat for conducting one or more queries. User input received via theinterface may simply comprise graphical points, lines, and/or polygonsoverlaid on a map of a geographical area, for example. In someembodiments, the points, lines, and/or polygons may be converted toand/or assigned to one or more geospatial and/or geo-referencedcoordinates such as a latitude and longitude coordinate, a GPScoordinate, and/or a certified location certificate, number, identifier,etc. The assigned geospatial coordinates/data may, in some embodiments,be utilized to determine one or more address and/or other locationalinformation that are required as keys and/or search or query terms toappropriately access and/or search the desired database schema. Thelocation keys and/or converted location data may then, for example, beutilized to conduct one or more queries to determine the value subjectdata appropriately associated with the user-defined and/or indicatedvalue subject(s).

In some embodiments, such as in the case that the user-defined locationdata is utilized to define one or more new value subjects at 806, forexample, data stored in association with certified (and/or other)locations associated with the new value subject may be determined. Thecoordinate boundaries of the new value subject may be determined, forexample, and utilized to query various data stored in one or moredatabases, such as historical claim loss data, weather data, crimereports, etc. In some embodiments, such as in the case that differingdata types and/or values are applicable to the user-defined valuesubject (e.g., the user input polygon includes portions of two landparcels and/or two different buildings), data totals, maximums,averages, minimums, medians, and/or other statistical and/ormathematical metrics based on the underlying data may be determined tobe applicable with respect to the user-defined value subject. Values ofvarious data variables applicable to locations encompassed by the valuesubject, for example, may be aggregated, analyzed, and/or otherwiseprocessed.

According to some embodiments, the method 800 may comprise providing(e.g., by the processing device and/or via the GUI and/or the electroniccommunications network) the value subject data, at 810. The datadetermined to be associated with the identified and/or defined valuesubject, for example, may be provided to one or more end-users, such asvia the interface (e.g., provided at 802). In some embodiments,different data types and/or values may be represented via the graphicaldepiction of the geographical area as different layers, colors, and/orother graphical attributes (e.g., different data heights or‘elevations’). In such a manner, for example, value subject data foruser-selected and/or defined value subjects may be readily (andaccurately) identified and provided to end-users, such as to facilitatevarious business decisions and/or processes (such as value subject riskassessment). In some embodiments, the end-user may be charged for accessto and/or for the provision of the value subject data. The method 800may comprise, in accordance with some embodiments for example, a valuesubject and/or certified location data sales portal method.

Referring now to FIG. 9, a diagram of a system 900 according to someembodiments is shown. In some embodiments, the system 900 may comprise auser device 902 comprising a display device 916 that outputs aninterface 920. The interface 920 may, for example, comprise graphicalrepresentations of one or more land parcels 922 a-b and/or a structure924 (e.g., being situated on a first land parcel 922 a). In someembodiments, a user (not explicitly shown, at least in full, in FIG. 9)of the system 900 and/or the user device 902 may provide input 926 a-bvia the interface 920. In the case that the display device 916 isconfigured to accept input, such as a touch screen display device and/ortouch-enabled display device, for example, as well as provide output,the user may provide input 926 a-b via the display device 916 and/or viathe interface 920 displayed thereby. In the case that the display device916 is configured only as an output device, the user may interact withand/or control an application executed by the user device 902 to defineinput 926 a-b to the interface 920.

In some embodiments, the user input 926 a-b may comprise point input 926a. The point input 926 a may, for example, comprise a point definedand/or selected by the user via the interface 920. In some embodiments,the point input 926 a may be provided (and accordingly received by theuser device 902) as an indication of a location for which the userdesires to acquire data. In the case of certified locations and/orcertified location data as described herein, for example, the user maychoose the point input 926 a as a representation of a location for whichvarious available data is desired. According to some embodiments, thepoint input 926 a may be utilized (e.g., by a processing device asdescribed herein) to look up relevant data associated with the indicatedlocation. In some embodiments, the point input 926 a may be utilized toidentify a value subject for which the user desires data and/or forwhich the user desires to perform a risk assessment. In the example ofFIG. 9, the point input 926 a may, for example, be determined toindicate that data is desired for a value object comprising the address“111 Main Street”. As depicted, the associated street address (such as abusiness or residence) actually resides in the same building 924 asseveral other addresses, even an address with a different street name.

According to some embodiments, such as described herein with respect tocertified location data processing and/or analysis, the value subjectdetermined to be associated with the point input 926 a may be determinedto comprise various extents (e.g., boundaries) based on stored rulesand/or preferences. In the case that the user desires to perform a riskassessment for the indicated address with respect to fire and/orbusiness operations, for example, it may be determined that the address“111 Main Street”, while residing in the same building 924 as “113 MainStreet”, “115 Main Street”, and “9333 Side Street”, is separated byfire-rated walls from the other addresses (e.g., the solid buildinglines in FIG. 9), and accordingly should be assessed individually,without respect to characteristics or data descriptive of the otheraddresses. In other words, the value subject associated with the pointinput 926 a may be determined to be defined by the boundaries of theaddress “111 Main Street”. In other cases, different rules may producedifferent results establishing and/or identifying different bounds foran associated value subject(s).

In some embodiments, for example, the user may provide (and such mayaccordingly be received by the user device 902) polygon input 926 b. Thepolygon input 926 b may comprise, for example, a free-hand drawn and/orinput shape of the interface 920 (e.g., defined by the user). Accordingto some embodiments, the polygon input 926 b may overlap, cross, and/orintersect multiple features of the interface 920 such as both the firstland parcel 922 a and the second land parcel 922 b as well as thestructure 924 and the street address “9333 Side Street”. In someembodiments, the polygon input 926 b may define an area for which theuser desires data. In such cases, the polygon data 926 b may be utilized(e.g., by the user device 902) to determine appropriate data thatcorresponds to any points or areas encompassed by the polygon input 926b. The user device 902 may, for example, retrieve data corresponding toweather event data for both land parcels 922 a-b, structural damage datafor the structure 924, and/or gross sales receipts data for a businessoperating at “9333 Side Street”. In some embodiments, the polygon input926 b may define a new value subject. In such embodiments, the datadetermined to be relevant to the bounded area may be analyzed withrespect to one or more various risks (or overall risk). In someembodiments, the polygon input 926 b (and/or point input 926 a) may beautomatically defined on behalf of the user, such as utilizing datadescriptive of the user's location (e.g., a location of the user device902). The polygon input 926 b may comprise, for example, a predetermineddiameter area around the current location of the user device 902, suchas determined via GPS and/or triangulation methodologies—e.g.,low-energy Bluetooth® iBeacon®-type technology that can pinpoint adevice location within a small and/or bounded area (e.g., within abuilding and/or store).

According to some embodiments, the value subject identified and/ordefined by the polygon input 926 b (and/or the data determined to beassociated therewith) may be determined and/or provided in accordancewith stored rules and/or preferences. In the case that the streetaddress of “9333 Side Street” is determined to be indicated by thepolygon input 926 b, for example, it may be determined that theappropriate corresponding value subject (predefined or new) should alsoinclude the area assigned to the street address “113 Main Street”. Thetwo address may, for example, be separated by a wall in which a door924-1 is disposed. The existence or usage of the door 924-1 may tiecertain or all risk metrics for the two addresses together (e.g., inaccordance with one rule such as a shared fire risk rule). In someembodiments, such as in the case that the “9333 Side Street” and the“115 Main Street” address are separated by a non-fire rated wall orpartition, those two addresses may similarly be joined and/or aggregatedwith respect to risk and/or other data. In some cases, such as in thecase that the polygon input 926 b overlaps two value subjects and/orareas or objects that do not share a particular risk, risk type, and/orrisk level or metric value, the user may be prompted (e.g., via theinterface 920) to select which addresses, structures 924, land parcels922 a-b, and/or other features, objects, and/or areas to include forpurposes of data retrieval, definition, and/or analysis.

In some embodiments, the interface 920 may comprise one or more inputbuttons 928 a-b. A first input button 928 a may, for example, comprise a“risk” button that the user may activate (e.g., via provided input) toinitiate risk (and/or other) analysis of desired locations—e.g., definedand/or indicated by one or more of the point input 926 a and/or thepolygon input 926 b. In some embodiments, the interface 920 may comprisea second input button 928 b, such as a purchase button that the user mayactivate (e.g., via provided input) to initiate payment for and/orpurchase of data (and/or analysis) desired with respect to the desiredlocation.

According to some embodiments, any or all of the components 902, 916,920, 922 a-b, 924, 926 a-b, 928 a-b of the system 900 may be similar inconfiguration and/or functionality to any similarly named and/ornumbered components described herein. Fewer or more components 902, 916,920, 922 a-b, 924, 926 a-b, 928 a-b and/or various configurations of thecomponents 902, 916, 920, 922 a-b, 924, 926 a-b, 928 a-b may be includedin the system 900 without deviating from the scope of embodimentsdescribed herein. While multiples of some components 922 a-b, 926 a-b,928 a-b are depicted and while single instances of other components 902,916, 920, 924 are depicted, for example, any component 902, 916, 920,922 a-b, 924, 926 a-b, 928 a-b depicted in the system 900 may comprise asingle device, a combination of devices and/or components 902, 916, 920,922 a-b, 924, 926 a-b, 928 a-b, and/or a plurality of devices, as is orbecomes desirable and/or practicable.

Referring now to FIG. 10, a flow diagram of a method 1000 according tosome embodiments is shown. According to some embodiments, the method1000 may be implemented, facilitated, and/or performed by or otherwiseassociated with any of the systems 100, 600 of FIG. 1 and/or FIG. 6herein.

In some embodiments, the method 1000 may be performed and/or implementedby and/or otherwise associated with one or more specialized and/orspecially-programmed computers, computer terminals, computer servers,computer systems and/or networks, and/or any combinations thereof (e.g.,by one or more third-party and/or insurance company and/or underwritercomputers, such as, e.g., the location processing device 110 of FIG. 1).

According to some embodiments, the method 1000 may comprise one or moreactions associated with location data 1002 a-n. The location data 1002a-n (e.g., certified location data) of one or more objects that may berelated to and/or otherwise associated with an insurance product and/orpolicy (e.g., one or more value subjects), for example, may be received(e.g., via user input), defined (e.g., based on user input), determined,calculated, looked-up, and/or derived. In some embodiments, the locationdata 1002 a-n may be gathered as raw data directly from one or morelocation data sources as described herein. One or more mapping and/orlocation determination products (e.g., maps, software, applications,and/or devices) may, for example, provide location data utilized todefine the location data 1002 a-n and/or may provide some or all of thelocation data 1002 a-n. In some embodiments, location data 1002 a-n maybe provided by an insured/policy holder and/or by a third-party (e.g.,cell phone tracking via GPS and/or social media “check-in”functionality; as received from the insured and/or from a third-partysuch as a GPS tracking provider and/or social media server), and/or maybe defined via a GUI that receives input from such users.

As depicted in FIG. 10, location data 1002 a-n from a plurality ofsources may be gathered. The plurality of location data 1002 a-n maycomprise information indicative of one or more locations of a pluralityof objects and/or types of objects. First location data 1002 a may, forexample, be descriptive of flood zone information descriptive of aparticular location (e.g., a location associated with a first valuesubject), while other location data 1002 n may be descriptive of weatherevents and/or probabilities for a location (e.g., a second valuesubject). In some embodiments, the first location data 1002 a may bedescriptive of a unique geo-coded location of a cellular telephone towerwhile other location data 1002 n may be descriptive of a specific roomin a hotel or a user-defined point, line, and/or polygon.

According to some embodiments, the method 1000 may also or alternativelycomprise one or more actions associated with location processing 1010.As depicted in FIG. 10, for example, some or all of the location data1002 a-n may be determined, gathered, and/or otherwise obtained forlocation processing 1010. In some embodiments, location processing 100may comprise aggregation, analysis, calculation, filtering, conversion,encoding and/or decoding (including encrypting and/or decrypting),sorting, ranking, and/or any combinations thereof. According to someembodiments, a processing device may execute specialized programinstructions to process the location data 1002 a-n to define a certifiedlocation and/or a value subject. Such a certified location and/or valuesubject may, for example, be descriptive (in a qualitative and/orquantitative manner) of a specific and/or unique location such as ageo-coded and/or geo-referenced point, line, area, and/or object/volume.

In some embodiments, location processing 1010 may comprisesophisticated, single variable or multivariate, single order ormulti-order location and/or certified location models and/or equationsthat analyze the location data 1002 a-n and correlate it to risks and/orlosses, and/or for any other desired purposes. In some embodiments,there may be other inputs, variables or events that may be stored inassociation with and/or comprise the location data 1002 a-n, such assevere weather events, natural disasters, evacuation warnings/alerts,catastrophic events, earthquakes, tornadoes, hurricanes, blizzards,mudslides, typhoons, wars, terrorist/enemy attacks, or the like. Suchcorrelations may be used, for example, to predict the level of riskand/or likely severity of injury and/or losses associated with one ormore certified locations and/or value subjects. In some embodiments,location data 1002 a-n may be utilized for planning crowd controlresources, natural or man-made resources, utilities, or infrastructuremanagement (e.g., water, electricity, fuel, etc.), or designing escapeor evacuation routes, or for any other desired purpose.

According to some embodiments, there may be a correlation between acertified location and/or value subject defined by the location data1002 a-n (and/or a portion thereof) and weather events when determiningrisk of loss. For example, a given certified location may correlate to ahigher risk when there is ice, snow, or rain likely to occur, than whenit is dry (e.g., if the certified location and/or value subjectcomprises a roadway segment or intersection).

In some embodiments, the method 1000 may also or alternatively compriseone or more actions associated with insurance underwriting 1020.Insurance underwriting 1020 may generally comprise any type, variety,and/or configuration of underwriting process and/or functionality thatis or becomes known or practicable. Insurance underwriting 1020 maycomprise, for example, simply consulting a pre-existing rule, criteria,and/or threshold to determine if an insurance product may be offered,underwritten and/or issued to customers, based on any relevant locationdata 1002 a-n. One example of an insurance underwriting 1020 process maycomprise one or more of risk assessment 1030 and/or premium calculation1040 (e.g., as shown in FIG. 10). In some embodiments, while both therisk assessment 1030 and the premium calculation 1040 are depicted asbeing part of an exemplary insurance underwriting 1020 procedure, eitheror both of the risk assessment 1030 and the premium calculation 1040 mayalternatively be part of a different process and/or different type ofprocess.

The location data 1002 a-n and/or a result of the location processing1010 may, for example, be determined and utilized to conduct riskassessment 1030 for any of a variety of purposes. In some embodiments(e.g., as shown), the risk assessment 1030 may be conducted as part of arating process for determining how to structure an insurance productand/or offering. A “rating engine” utilized in an insurance underwritingprocess may, for example, retrieve location data 1002 a-n and/or aresult of the location processing 1010 for input into a calculation(and/or series of calculations and/or a mathematical model) to determinea level of risk likely to be associated with a particular value subject(e.g., an area, device, structure, a plurality of associated (proximate,common ownership, etc.) structures, points, coordinates, and/orlocations, an entity, etc.).

According to some embodiments, the method 1000 may also or alternativelycomprise one or more actions associated with premium calculation 1040(e.g., which may be part of the insurance underwriting 1020). In thecase that the method 1000 comprises the insurance underwriting 1020process, for example, the risk assessment 1030 may be utilized by a“pricing engine” to calculate (and/or look-up or otherwise determine) anappropriate premium to charge for an insurance policy associated withthe object for which the location data 1002 a-n was collected and forwhich the risk assessment 1030 was performed. In some embodiments, theobject analyzed may comprise an object (e.g., a value subject) for whichan insurance product is sought (e.g., the analyzed object may comprise abuilding for which a home-owners policy is desired or a location forwhich business insurance is desired). According to some embodiments, theobject analyzed may be an object other than the object for whichinsurance is sought (e.g., the analyzed object may comprise a buildingin which a business for which a business insurance policy is desired islocated—or located adjacent to or in proximity to). In some embodiments,the risk assessment 1030 may comprise a comparison of a determined risklevel or rating to one or more stored rules and/or thresholds (e.g., arisk aggregation limit). If it is determined that a particular structure(and/or plurality of structures, location, and/or area) exceeds aparticular risk limit, for example, the method 1000 may be altered,informed, and/or influenced. A decision may be made with respect to anew insurance policy for the same structure or other object, forexample, to (i) not underwrite the policy, and/or (ii) alter or set thepremium and/or deductible (or other product parameters) based on theover-limit situation.

According to some embodiments, the method 1000 may also or alternativelycomprise one or more actions associated with insurance policy quoteand/or issuance 1050. Once a policy has been rated, priced or quoted andthe customer has accepted the coverage terms, the insurance company may,for example, bind and issue the policy by hard copy and/orelectronically to the customer/insured.

In general, a customer may visit a website and/or an insurance agent,for example, provide the needed information about the customer and typeof desired insurance, and request an insurance policy and/or product.According to some embodiments, the insurance underwriting 1020 isperformed utilizing information about the potential insured and thepolicy is issued based on the result thereof. Insurance coverage may,for example, be evaluated, rated, priced, and/or sold to one or morecustomers, at least in part based on the location data 1002 a-n. In someembodiments, an insurance company may have the potential customerindicate electronically, on-line, or otherwise whether they have anylocation sensing devices (and/or which specific devices they have)and/or whether they are willing to install them or have them installed.In some embodiments, this may be done by check boxes, radio buttons, orother form of data input/selection, on a web page and/or via a mobiledevice application. In some embodiments, customer and/or user locationdata may be provided via a GUI such as by the customer/user identifying,selecting, and/or drawing or otherwise defining one or more points,liens, and/or polygons (e.g., drawn in a user-defined manner on amap-based interface).

According to some embodiments, the method 1000 may also or alternativelycomprise one or more actions associated with claims 1060. In theinsurance context, for example, after an insurance product is providedand/or policy is issued (e.g., via the insurance policy quote andissuance 1050), one or more insurance claims may be filed against theproduct/policy. In some embodiments (as depicted in FIG. 10), such as inthe case that a first object associated with the insurance policy issomehow involved with one or more insurance claims 1060, first locationdata 1002 a of the value subject or related objects may be gatheredand/or otherwise obtained. According to some embodiments, such locationdata 1002 a-n may comprise data indicative of the uniquely-identifiablelocation of the object at the time of casualty or loss (e.g., as definedby the one or more claims 1060). Information on claims may be providedto the location processing 1010, risk assessment 1030, and/or premiumcalculation 1040 to update, improve, and/or enhance these proceduresand/or devices. According to some embodiments, the first location data1002 a may be utilized to plan, manage, and/or conduct catastrophe(“CAT”) response activities. In the case of a claim event involving aparticular structure (e.g., a certified location and/or value subject),for example, stored attributes of the structure (such as age,construction type, configuration) may be utilized to determine,identify, and/or select one or more CAT response agents havingexpertise, training, and/or knowledge relating to one or more particularattributes of the structure. According to some embodiments, such as inthe case of a multi-customer loss event (e.g., a catastrophic event suchas a flood, explosion, earth quake, etc.), the first location data 1002a may allow rapid and/or accurate prediction of the extent of the lossevent (e.g., with respect to a particular company and/or customer-base).A disaster zone (such as one or more of the fall risk zone 220, theterrorist risk zone 222, and/or the weather risk zone 224 of FIG. 2D)may be compared to the first location data 1002 a to determine, forexample, how many customers and/or customer accounts are likely to beaffected, the total possible or likely magnitude of loss (e.g., anaggregation of policy limits in the affected zone), and/or the type(s)of loss. In such a manner, for example, the magnitude, extent, and/ortype of CAT response may be planned, determined, and/or adjusted to suitthe particular loss event.

In some embodiments, the method 1000 may also or alternatively compriseinsurance policy renewal review 1070. Location data 1002 a-n may beutilized, for example, to determine if and/or how an existing insurancepolicy (e.g., provided via the insurance policy quote and issuance 1050)may be renewed. According to some embodiments, such as in the case thatan insured is involved with and/or in charge of (e.g., responsible for)providing the location data 1002 a-n, a review may be conducted todetermine if the correct amount, frequency, and/or type or quality ofthe location data 1002 a-n was indeed provided by the insured during theoriginal term of the policy. In the case that the location data 1002 a-nwas lacking, the policy may not, for example, be renewed and/or anydiscount received by the insured for providing the location data 1002a-n may be revoked or reduced. In some embodiments, the customer may beoffered a discount for having certain location sensing devices or beingwilling to install them or have them installed (e.g., a willingness oracceptance of “push” notifications from Bluetooth® devices such asiBeacons® available from Apple®, Inc. of Cupertino, Calif.; or bewilling to adhere to certain thresholds based on measurements from suchdevices).

According to some embodiments, the method 1000 may also or alternativelycomprise one or more actions associated with risk/loss control 1080. Anyor all data gathered as part of a claims 1060 process, for example, maybe gathered, collected, and/or analyzed to determine how (if at all) oneor more of a rating engine (e.g., the risk assessment 1030), a pricingengine (e.g., the premium calculation 1040), the insurance underwriting1020 process, and/or the location processing 1010 itself, should beupdated to reflect actual and/or realized risk, costs, and/or otherissues associated with the location data 1002 a-n. Results of therisk/loss control 1080 may, according to some embodiments, be fed backinto the method 1000 to refine the risk assessment 1030, the premiumcalculation 1040 (e.g., for subsequent insurance queries and/orcalculations), the insurance policy renewal review 1070 (e.g., are-calculation of an existing policy for which the one or more claims1060 were filed), and/or the location processing 1010 to appropriatelyscale the output of the risk assessment 1080.

Turning now to FIG. 11, a flow diagram of a method 1100 according tosome embodiments is shown. In some embodiments, the method 1100 maycomprise a certified location-based risk assessment method which may,for example, be described as a “rating engine”. According to someembodiments, the method 1100 may be implemented, facilitated, and/orperformed by or otherwise associated with any of the systems 100, 600 ofFIG. 1 and/or FIG. 6 herein. In some embodiments, the method 1100 may beassociated with the method 1000 of FIG. 10. The method 1100 may, forexample, comprise a portion of the method 1000 such as the riskassessment 1030.

According to some embodiments, the method 1100 may comprise determiningone or more loss frequency distributions for a class of objects, at1102. In some embodiments, a first loss frequency distribution may bedetermined, at 1102 a, based on location data and/or metrics. Locationdata (such as the location data 1002 a-n of FIG. 10) for an object classand/or type may, for example, be analyzed to determine relationshipsbetween location data and/or metrics and empirical data descriptive ofactual insurance losses for the object types and/or classes of objects(e.g., a type and/or class of value subjects). A location dataprocessing and/or analytics system (e.g., the location processing device110 and/or the system 600 as described with respect to FIG. 1 and/orFIG. 6 herein) may, according to some embodiments, conduct regressionand/or other mathematical analysis on various location metrics todetermine and/or identify mathematical relationships that may existbetween such metrics and actual sustained losses and/or casualties.

Similarly, at 1102 b, a second loss frequency distribution may bedetermined based on non-location data. According to some embodiments,the determining at 1102 b may comprise a standard or typical lossfrequency distribution utilized by an entity (such as an insurancecompany) to assess risk. The non-location metrics utilized as inputs inthe determining at 1102 b may include, for example, age of a building orcar, driving record of an individual, a criminal record of anindividual, color of a vehicle, etc. In some embodiments, the lossfrequency distribution determinations at 1102 a-b may be combined and/ordetermined as part of a single comprehensive loss frequency distributiondetermination. In such a manner, for example, expected total lossprobabilities (e.g., taking into account both location data andnon-location data) for an object type and/or class (e.g., a type and/orclass of value subjects) may be determined. In some embodiments, thismay establish and/or define a baseline, datum, average, and/or standardwith which individual risk assessments may be measured.

According to some embodiments, the method 1100 may comprise determiningone or more loss severity distributions for a class of objects, at 1104.In some embodiments, a first loss severity distribution may bedetermined, at 1104 a, based on location data and/or metrics. Locationdata (such as the location data 1002 a-n of FIG. 10) for a class ofobjects and/or for a particular type of object may (e.g., a type and/orclass of value subjects), for example, be analyzed to determinerelationships between various location data and/or metrics and empiricaldata descriptive of actual insurance losses for such object types and/orclasses of objects. A certified location processing and/or analyticssystem (e.g., the location processing device 110 and/or the system 600as described with respect to FIG. 1 and/or FIG. 6 herein) may, accordingto some embodiments, conduct regression analysis on various certifiedlocation metrics to determine and/or identify mathematical relationshipsthat may exist between such metrics and actual sustained losses and/orcasualties.

Similarly, at 1104 b, a second loss severity distribution may bedetermined based on non-location data. According to some embodiments,the determining at 1104 b may comprise a standard or typical lossseverity distribution utilized by an entity (such as an insuranceagency) to assess risk. The non-location metrics utilized as inputs inthe determining at 1104 b may include, for example, cost of replacementor repair, ability to self-mitigate loss (e.g., if a building has a firesuppression system and/or automatically closing fire doors), etc. Insome embodiments, the loss severity distribution determinations at 1104a-b may be combined and/or determined as part of a single comprehensiveloss severity distribution determination. In such a manner, for example,expected total loss seventies (e.g., taking into account both locationdata and non-location data) for a particular object type and/or class(e.g., a type and/or class of value subjects) may be determined. In someembodiments, this may also or alternatively establish and/or define abaseline, datum, average, and/or standard with which individual riskassessments may be measured.

In some embodiments, the method 1100 may comprise determining one ormore expected loss frequency distributions for a specific object in theclass of objects (e.g., a type and/or class of value subjects), at 1106.Regression and/or other mathematical analysis performed on the locationloss frequency distribution derived from empirical data, at 1102 a forexample, may identify various location metrics and may mathematicallyrelate such metrics to expected loss occurrences (e.g., based onhistorical trends). Based on these relationships, a location lossfrequency distribution may be developed at 1106 a for the specificobject. In such a manner, for example, known location metrics for aspecific object may be utilized to develop an expected distribution(e.g., probability) of occurrence of location-related loss for thespecific object (e.g., a specific value subject).

Similarly, regression and/or other mathematical analysis performed onthe non-location loss frequency distribution derived from empiricaldata, at 1102 b for example, may identify various non-location metricsand may mathematically relate such metrics to expected loss occurrences(e.g., based on historical trends). Based on these relationships, anon-location loss frequency distribution may be developed at 1106 b forthe specific object (e.g., the specific value subject). In such amanner, for example, known non-location metrics for a specific objectmay be utilized to develop an expected distribution (e.g., probability)of occurrence of non-location-related loss for the specific object. Insome embodiments, the non-location loss frequency distributiondetermined at 1106 b may be similar to a standard or typical lossfrequency distribution utilized by an insurer to assess risk.

In some embodiments, the method 1100 may comprise determining one ormore expected loss severity distributions for a specific object in theclass of objects (e.g., a specific value subject and/or class of valuesubjects), at 1108. Regression and/or other mathematical analysisperformed on the location loss severity distribution derived fromempirical data, at 1104 a for example, may identify various locationmetrics and may mathematically relate such metrics to expected lossseverities (e.g., based on historical trends). Based on theserelationships, a location loss severity distribution may be developed at1108 a for the specific object. In such a manner, for example, knownlocation metrics for a specific object may be utilized to develop anexpected severity for occurrences of location-related loss for thespecific object.

Similarly, regression and/or other mathematical analysis performed onthe non-location loss severity distribution derived from empirical data,at 1104 b for example, may identify various non-location metrics and maymathematically relate such metrics to expected loss severities (e.g.,based on historical trends). Based on these relationships, anon-location loss severity distribution may be developed at 1108 b forthe specific object. In such a manner, for example, known non-locationmetrics for a specific object may be utilized to develop an expectedseverity of occurrences of non-location-related loss for the specificobject. In some embodiments, the non-location loss severity distributiondetermined at 1108 b may be similar to a standard or typical lossfrequency distribution utilized by an insurer to assess risk.

It should also be understood that the location-based determinations 1102a, 1104 a, 1106 a, 1108 a and non-location-based determinations 1102 b,1104 b, 1106 b, 1108 b are separately depicted in FIG. 11 for ease ofillustration of one embodiment descriptive of how certified locationmetrics may be included to enhance standard risk assessment procedures.According to some embodiments, the location-based determinations 1102 a,1104 a, 1106 a, 1108 a and non-location-based determinations 1102 b,1104 b, 1106 b, 1108 b may indeed be performed separately and/ordistinctly in either time or space (e.g., they may be determined bydifferent software and/or hardware modules or components and/or may beperformed serially with respect to time). In some embodiments, thelocation-based determinations 1102 a, 1104 a, 1106 a, 1108 a andnon-location-based determinations 1102 b, 1104 b, 1106 b, 1108 b may beincorporated into a single risk assessment process or “engine” that may,for example, comprise a risk assessment software program, package,and/or module.

In some embodiments, the method 1100 may comprise calculating a riskscore for the object (e.g., a specific value subject), at 1110.According to some embodiments, formulas, charts, and/or tables may bedeveloped that associate various location and/or non-location metricmagnitudes with risk scores. High numbers of policy holders on aparticular parcel of land may be represented by a first certifiedlocation metric, for example, that may equate to a risk score of two(2), while high amounts of existing risk exposure in a particularbuilding (e.g., in a high-risk earthquake area) represented by a secondlocation metric may equate to a risk score of ten (10). Risk scores fora plurality of location and/or non-location metrics may be determined,calculated, tabulated, and/or summed to arrive at a total risk score forthe object and/or for an object class. According to some embodiments,risk scores may be derived from the location and/or non-location lossfrequency distributions and the location and/or non-location lossseverity distribution determined at 1106 a-b and 1108 a-b, respectively.More details on one similar method for assessing risk are provided inApplicants' U.S. Pat. No. 7,330,820 entitled “PREMIUM EVALUATION SYSTEMSAND METHODS” which issued on Feb. 12, 2008, the risk assessment conceptsand descriptions of which are hereby incorporated by reference herein.

In some embodiments, the results of the method 1100 may be utilized todetermine a premium for an insurance policy for the specific objectanalyzed. Any or all of the location and/or non-location loss frequencydistributions of 1106 a-b, the location and/or non-location lossseverity distributions of 1108 a-b, and the risk score of 1110 may, forexample, be passed to and/or otherwise utilized by a premium calculationprocess via the node labeled “A” in FIG. 11.

Referring to FIG. 12, for example, a flow diagram of a method 1200 (thatmay initiate at the node labeled “A”) according to some embodiments isshown. In some embodiments, the method 1200 may comprise a locationand/or certified location-based premium determination method which may,for example, be described as a “pricing engine”. According to someembodiments, the method 1200 may be implemented, facilitated, and/orperformed by or otherwise associated with any of the systems 100, 600 ofFIG. 1 and/or FIG. 6 herein. In some embodiments, the method 1200 may beassociated with the method 1000 of FIG. 10. The method 1200 may, forexample, comprise a portion of the method 1000 such as the premiumcalculation 1040. Any other technique for calculating an insurancepremium that utilizes location and/or certified location informationdescribed herein may be utilized in accordance with some embodiments.

In some embodiments, the method 1200 may comprise determining a purepremium, at 1202. A pure premium is a basic, unadjusted premium that isgenerally calculated based on loss frequency and severity distributions.According to some embodiments, the location and/or non-location lossfrequency distributions (e.g., from 1106 a-b in FIG. 11) and thelocation and/or non-location loss severity distributions (e.g., from1108 a-b in FIG. 11) may be utilized to calculate a pure premium thatwould be expected, mathematically, to result in no net gain or loss forthe insurer when considering only the actual cost of the loss or lossesunder consideration and their associated loss adjustment expenses.Determination of the pure premium may generally comprise simulationtesting and analysis that predicts (e.g., based on the suppliedfrequency and severity distributions) expected total losses(location-based and/or non-location-based) over time.

According to some embodiments, the method 1200 may comprise determiningan expense load, at 1204. The pure premium determined at 1202 may nottake into account operational realities experienced by an insurer. Thepure premium does not account, for example, for operational expensessuch as overhead, staffing, taxes, fees, etc. Thus, in some embodiments,an expense load (or factor) is determined and utilized to take suchcosts into account when determining an appropriate premium to charge foran insurance product. According to some embodiments, the method 1200 maycomprise determining a risk load, at 1206. The risk load is a factordesigned to ensure that the insurer maintains a surplus amount largeenough to produce an expected return for an insurance product.

According to some embodiments, the method 1200 may comprise determininga total premium, at 1208. The total premium may generally be determinedand/or calculated by summing or totaling one or more of the purepremium, the expense load, and the risk load. In such a manner, forexample, the pure premium is adjusted to compensate for real-worldoperating considerations that affect an insurer.

According to some embodiments, the method 1200 may comprise grading thetotal premium, at 1210. The total premium determined at 1208, forexample, may be ranked and/or scored by comparing the total premium toone or more benchmarks. In some embodiments, the comparison and/orgrading may yield a qualitative measure of the total premium. The totalpremium may be graded, for example, on a scale of “A”, “B”, “C”, “D”,and “F”, in order of descending rank. The rating scheme may be simpleror more complex (e.g., similar to the qualitative bond and/or corporatecredit rating schemes determined by various credit ratings agencies suchas Standard & Poors' (S&P) Financial service LLC, Moody's InvestmentService, and/or Fitch Ratings from Fitch, Inc., all of New York, N.Y.)of as is or becomes desirable and/or practicable. More details on onesimilar method for calculating and/or grading a premium are provided inApplicants' U.S. Pat. No. 7,330,820 entitled “PREMIUM EVALUATION SYSTEMSAND METHODS” which issued on Feb. 12, 2008, the premium calculation andgrading concepts and descriptions of which are hereby incorporated byreference herein.

According to some embodiments, the method 1200 may comprise outputtingan evaluation, at 1212. In the case that the results of thedetermination of the total premium at 1208 are not directly and/orautomatically utilized for implementation in association with aninsurance product, for example, the grading of the premium at 1210and/or other data such as the risk score determined at 1110 of FIG. 11may be utilized to output the evaluation, e.g., an indication of thedesirability and/or expected profitability of implementing thecalculated premium. The outputting of the evaluation may be implementedin any form or manner that is or becomes known or practicable. One ormore recommendations, graphical representations, visual aids,comparisons, and/or suggestions may be output, for example, to a device(e.g., a server and/or computer workstation) operated by an insuranceunderwriter and/or sales agent. One example of an evaluation comprises acreation and output of a risk matrix which may, for example, bydeveloped utilizing Enterprise Risk Register® software which facilitatescompliance with ISO 17799/ISO 27000 requirements for risk mitigation andwhich is available from Northwest Controlling Corporation Ltd. (NOWECO)of London, UK.

Turning to FIG. 13, for example, a diagram of an exemplary risk matrix1300 according to some embodiments is shown. In some embodiments (asdepicted), the risk matrix 1300 may comprise a simple two-dimensionalgraph having an x-axis and a y-axis. Any other type of risk matrix, orno risk matrix, may be used if desired. The detail, complexity, and/ordimensionality of the risk matrix 1300 may vary as desired and/or may betied to a particular insurance product or offering. In some embodiments,the risk matrix 1300 may be utilized to visually illustrate arelationship between the risk score (e.g., from 1030 of FIG. 10 and/orfrom 1110 of FIG. 11) of an object and the total determined premium(e.g., from 1040 of FIG. 10 and/or 1208 of FIG. 12; and/or a gradingthereof, such as from 1210 of FIG. 12) for an insurance product offeredin relation to the object (e.g., value subject). As shown in FIG. 13,for example, the premium grade may be plotted along the x-axis of therisk matrix 1300 and/or the risk score may be plotted along the y-axisof the risk matrix 1300.

In such a manner, the risk matrix 1300 may comprise four (4) quadrants1302 a-d (e.g., similar to a “four-square” evaluation sheet utilized byautomobile dealers to evaluate the propriety of various possible pricing“deals” for new automobiles). The first quadrant 1302 a represents themost desirable situations where risk scores are low and premiums arehighly graded. The second quadrant 1302 b represents less desirablesituations where, while premiums are highly graded, risk scores arehigher. Generally, object-specific data that results in data pointsbeing plotted in either of the first two quadrants 1302 a-b isindicative of an object for which an insurance product may be offered onterms likely to be favorable to the insurer. The third quadrant 1302 crepresents less desirable characteristics of having poorly gradedpremiums with low risk scores and the fourth quadrant 1302 d representsthe least desirable characteristics of having poorly graded premiums aswell as high risk scores. Generally, object-specific data that resultsin data points being plotted in either of the third and fourth quadrants1302 c-d is indicative of an object for which an insurance productoffering is not likely to be favorable to the insurer

One example of how the risk matrix 1300 may be output and/or implementedwith respect to certified location of a value subject will now bedescribed. Assume, for example, that a home owner's policy is desired bya consumer and/or that a home owner's policy product is otherwiseanalyzed to determine whether such a policy would be beneficial for aninsurer to issue. Typical risk metrics such as the age of the building,a city in which the building is located, and/or a type of constructionof the building or insulation values (e.g., energy ratings) may beutilized to produce expected loss frequency and loss severitydistributions (such as determined at 1106 b and 1108 b of FIG. 11).

In some embodiments, certified location metrics of the building (i.e.,the value subject, i.e., object being insured) such as how many otherpolicies have been written for the building (e.g., current, historical,for the same customer and/or for different customers), whether thebuilding (or a portion thereof) falls within (or outside of) aparticular zone (such as a flood zone, busyness zone (e.g., as describedin U.S. patent application Ser. No. 12/978,535 titled “RISK ASSESSMENTAND CONTROL, INSURANCE PREMIUMS DETERMINATIONS, AND OTHER APPLICATIONSUSING BUSINESS” filed on Dec. 24, 2010 and Published as U.S. PatentApplication Publication No. 2001/0161119 on Jun. 30, 2011, the busynessconcepts and descriptions of which are hereby incorporated by referenceherein), and/or risk zone (e.g., as described in U.S. patent applicationSer. No. 13/334,897 titled “SYSTEMS AND METHODS FOR CUSTOMER-RELATEDRISK ZONES” filed on Dec. 22, 2011 and/or U.S. patent application Ser.No. 13/335,476 titled “SYSTEMS AND METHODS FOR CLIENT-RELATED RISKZONES” filed on Dec. 22, 2011, the risk zone concepts and descriptionsof each of which are hereby incorporated herein by reference)), whatother buildings or features are located on the same parcel of land,and/or a history of loss for the building (e.g., for the same customerand/or for different customers) may also be utilized to produce expectedlocation loss frequency and location loss severity distributions (suchas determined at 1106 a and 1108 a of FIG. 11). In some embodiments,location and/or certified location metrics of objects other thanbuilding (i.e., other than the object being insured) such as structuralstability of an adjacent building, business activity types in adjacentapartments/units and/or buildings, and/or characteristics of a parcel ofland upon which the building is situated and/or an adjacent or proximateparcel of land may also or alternatively be utilized to produce expectedlocation loss frequency and location loss severity distributions (suchas determined at 1106 a and 1108 a of FIG. 11). According to someembodiments, singular loss frequency and loss severity distributions maybe determined utilizing both typical risk metrics as well as locationand/or certified location metrics (of the object being insured and/or ofother associated objects).

In the case that the building houses a plurality of high-risk businesses(e.g., other than or including a business of the potential insured),especially when compared to typical buildings of the same type, the riskscore for the building may be determined to be relatively high, such asseventy-five (75) on a scale from zero (0) to one hundred (100). Ofcourse other non-location factors such as the age of the consumer and/orthe number of inhabitants (and/or other factors) may also contribute tothe risk score for the building, consumer, and/or insurance productassociated therewith. Also, if the typical times of day and/or days ofthe week are known for when particular high-risk activities occur at ornear the building (e.g., on the same parcel of land), this can becorrelated with historical and/or predicted risk levels of the buildingat those times of day to provided more accurate risk scores.

The total premium calculated for a potential insurance policy offeringcovering the building (e.g., determined at 1208 of FIG. 12) may, tocontinue the example, be graded between “B” and “C” (e.g., at 1210 ofFIG. 12) or between “Fair” and “Average”. The resulting combination ofrisk score and premium rating may be plotted on the risk matrix 1300, asrepresented by a data point 1304 shown in FIG. 13. The data point 1304,based on the location-influenced risk score and the accordinglycertified location-influenced premium calculation, is plotted in thesecond quadrant 1102 b, in a position indicating that while the risk ofinsuring the building/consumer is relatively high, the calculatedpremium is probably large enough to compensate for the level of risk. Insome embodiments, an insurer may accordingly look favorably upon issuingsuch as insurance policy to the consumer to cover the building/home inquestion and/or may consummate a sale of such a policy to the consumer(e.g., based on the evaluation output at 1212 of FIG. 12, such asdecision and/or sale may be made).

Turning now to FIG. 14, a flow diagram of a method 1400 according tosome embodiments is shown. In some embodiments, the method 1400 maycomprise a location-based risk loss control method. According to someembodiments, the method 1400 may be implemented, facilitated, and/orperformed by or otherwise associated with any of the systems 100, 600 ofFIG. 1 and/or FIG. 6 herein. In some embodiments, the method 1400 may beassociated with the method 1000 of FIG. 10. The method 1400 may, forexample, comprise a portion of the method 1000 such as the risk/losscontrol 1080. In some embodiments, the method 1400 may also oralternatively be associated with any of the methods 1100, 1200 describedin relation to FIG. 11 and/or FIG. 12 herein. The method 1400 maycomprise, in some embodiments for example, a continuation of thelocation-based risk assessment method 1100 of FIG. 11 and/or thelocation-based premium determination method 1200 of FIG. 12.

According to some embodiments, the method 1400 may comprise receivingclaim information, at 1402. A claim may be received from aninsured/policy holder with respect to a loss or casualty (e.g., anaccident or any other loss event) sustained with respect to an objectcovered by an insurance policy for which an insurance company receivespremiums, for example. In some embodiments, the claim may be made withrespect to an insurance product for which an evaluation was output(e.g., during a sales and/or underwriting process) such as at 1212 ofFIG. 12. The claim information may generally comprise and/or indicatedata descriptive of the loss such as severity and/or cause, or otherinformation.

In some embodiments, the method 1400 may comprise receiving a locationmetric associated with the claim, at 1404. Information descriptive of alocation and/or certified location metric and/or raw location and/orcertified location data associated with the severity and/or cause of theloss, for example, may be provided by the consumer as part of a claimsprocess (e.g., may be received in conjunction with the informationreceived at 1402). According to some embodiments, the location dataand/or metric may be received from sources other than the consumer.Returning to the example of the insured building described with respectto FIG. 13, for example, the homeowner may not have access to and/or maynot be capable of properly determining a number of inhabitants of thebuilding where (and when) an accident occurred (e.g., in the case of anapartment building). Thus, in some embodiments, the insurer and/or athird-party may utilize the claim information to locate, identify,and/or retrieve certified location data for the building where theaccident occurred and at and/or around the time of the accident or loss.According to some embodiments, the consumer may provide suchinformation, the building may be configured to provide desired claimand/or location information, a third-party data provider suchinformation, and/or the insurance company may get it directly from asensing device.

In some embodiments, the method 1400 may comprise processing the claiminformation and the location information, at 1406. The loss informationand/or the location information may be combined with previous and/orhistoric loss data and/or location information, for example, to define anew set of data that may be utilized to assess risk and/or determinepremiums for new insurance policies and/or products, and/or may beutilized to update risk and/or pricing for one or more existing policies(such as the policy of the example home owner), or it may be utilized toupdate how the location information is determined based on specificlocation data/sensors.

The method 1400 may, for example, update a rating engine at 1408 and/orupdate a pricing engine at 1410. According to some embodiments, the newloss information and/or the new loss-related location and/or certifiedlocation information may be fed back into one or more of the ratingengine and the pricing engine utilized by an insurer to evaluate and/orstructure insurance products and pricing thereof. The location and/orcertified location-based risk assessment method of 1100 of FIG. 11and/or the location and/or certified location-based premiumdetermination method 1200 of FIG. 12 may, for example, be conductedand/or re-conducted, based on the newly available claim and/orclaim-related location information. In such a manner, insurance policyrisk analysis and/or pricing may be updated to reflect the most recentdata available, increasing the probability that the risk and pricingmodels will maintain appropriate levels of accuracy.

Turning to FIG. 15, a block diagram of an apparatus 1500 according tosome embodiments is shown. In some embodiments, the apparatus 1500 maybe similar in configuration and/or functionality to any of the locationprocessing device 110 of FIG. 1 and/or may comprise a portion of thesystem 600 of FIG. 6 herein. The apparatus 1500 may, for example,execute, process, facilitate, and/or otherwise be associated with any ofthe methods 400, 700, 800, 1000, 1100, 1200, 1400 described inconjunction with FIG. 4, FIG. 7, FIG. 8, FIG. 10, FIG. 11, FIG. 12,and/or FIG. 14 herein. In some embodiments, the apparatus 1500 maycomprise a processing device 1512, an input device 1514, an outputdevice 1516, a communication device 1518, an interface 1520, a memorydevice 1540 (storing various programs and/or instructions 842 and data844), and/or a cooling device 1550. According to some embodiments, anyor all of the components 1512, 1514, 1516, 1518, 1520, 1540, 1542, 1544,1550 of the apparatus 1500 may be similar in configuration and/orfunctionality to any similarly named and/or numbered componentsdescribed herein. Fewer or more components 1512, 1514, 1516, 1518, 1520,1540, 1542, 1544, 1550 and/or various configurations of the components1512, 1514, 1516, 1518, 1520, 1540, 1542, 1544, 1550 may be included inthe apparatus 1500 without deviating from the scope of embodimentsdescribed herein.

According to some embodiments, the processing device 1512 may be orinclude any type, quantity, and/or configuration of electronic and/orcomputerized processor that is or becomes known. The processing device1512 may comprise, for example, an Intel® IXP 2800 network processor oran Intel® XEON™ Processor coupled with an Intel® E7501 chipset. In someembodiments, the processing device 1512 may comprise multipleinter-connected processors, microprocessors, and/or micro-engines.According to some embodiments, the processing device 1512 (and/or theapparatus 1500 and/or portions thereof) may be supplied power via apower supply (not shown) such as a battery, an Alternating Current (AC)source, a Direct Current (DC) source, an AC/DC adapter, solar cells,and/or an inertial generator. In the case that the apparatus 1500comprises a server such as a blade server, necessary power may besupplied via a standard AC outlet, power strip, surge protector, and/orUninterruptible Power Supply (UPS) device.

In some embodiments, the input device 1514 and/or the output device 1516are communicatively coupled to the processing device 1512 (e.g., viawired and/or wireless connections and/or pathways) and they maygenerally comprise any types or configurations of input and outputcomponents and/or devices that are or become known, respectively. Theinput device 1514 may comprise, for example, a keyboard that allows anoperator of the apparatus 1500 to interface with the apparatus 1500(e.g., by a consumer, such as to purchase value subject data and/orinsurance policies priced utilizing certified location data and/or by anunderwriter and/or insurance agent, such as to evaluate risk and/orcalculate premiums for an insurance policy). In some embodiments, theinput device 1514 may comprise a sensor configured to provideinformation such as encoded certified location information to theapparatus 1500 and/or the processing device 1512. The output device 1516may, according to some embodiments, comprise a display screen and/orother practicable output component and/or device. The output device 1516may, for example, provide insurance and/or investment pricing and/orrisk analysis to a potential customer (e.g., via a website) and/or to anunderwriter or sales agent attempting to structure an insurance (and/orinvestment) product (e.g., via a computer workstation), such as via theinterface 1520. According to some embodiments, the input device 1514and/or the output device 1516 may comprise and/or be embodied in asingle device such as a touch-screen monitor.

In some embodiments, the communication device 1518 may comprise any typeor configuration of communication device that is or becomes known orpracticable. The communication device 1518 may, for example, comprise aNetwork Interface Card (NIC), a telephonic device, a cellular networkdevice, a router, a hub, a modem, and/or a communications port or cable.In some embodiments, the communication device 1518 may be coupled toprovide data to a customer device (not shown in FIG. 15), such as in thecase that the apparatus 1500 is utilized as a certified location and/orvalue subject data portal. The communication device 1518 may, forexample, comprise a cellular telephone network transmission device thatsends signals indicative of certified locations and/or value subjectdata to customer and/or subscriber handheld, mobile, and/or telephonedevices. According to some embodiments, the communication device 1518may also or alternatively be coupled to the processing device 1512. Insome embodiments, the communication device 1518 may comprise an IR, RF,Bluetooth™, NFC, and/or Wi-Fi® network device coupled to facilitatecommunications between the processing device 1512 and another device(such as a customer device and/or a third-party device).

The memory device 1540 may comprise any appropriate information storagedevice that is or becomes known or available, including, but not limitedto, units and/or combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, and/or semiconductor memorydevices such as Random Access Memory (RAM) devices, Read Only Memory(ROM) devices, Single Data Rate Random Access Memory (SDR-RAM), DoubleData Rate Random Access Memory (DDR-RAM), and/or Programmable Read OnlyMemory (PROM). The memory device 1540 may, according to someembodiments, store one or more of certified location instructions1542-1, risk assessment instructions 1542-2, premium determinationinstructions 1542-3, location data 1544-1, value subject data 1544-2,customer data 1544-3, and/or claim/loss data 1544-4. In someembodiments, the certified location instructions 1542-1, the riskassessment instructions 1542-2, and/or the premium determinationinstructions 1542-3 may be utilized by the processing device 1512 toprovide output information via the output device 1516 (and/or interface1520) and/or the communication device 1518 (e.g., the risk matrix 1300of FIG. 13).

According to some embodiments, the certified location instructions1542-1 may be operable to cause the processing device 1512 to processlocation data 1544-1, value subject data 1544-2, customer data 1544-3,and/or claim/loss data 1544-4 as described herein. Location data 1544-1,value subject data 1544-2, customer data 1544-3, and/or claim/loss data1544-4 received via the input device 1514 and/or the communicationdevice 1518 may, for example, be analyzed, sorted, filtered, decoded,decompressed, ranked, scored, plotted, and/or otherwise processed by theprocessing device 1512 in accordance with the certified locationinstructions 1542-1. In some embodiments, raw location data 1544-1descriptive of various location-based attributes of an object and/orarea and/or other location data 1544-1, value subject data 1544-2,customer data 1544-3, and/or claim/loss data 1544-4 may be fed by theprocessing device 1512 through one or more mathematical and/orstatistical formulas and/or models in accordance with the certifiedlocation instructions 1542-1 to define one or more certified locationsand/or associated data relationships that may then be utilized forvarious purposes as described herein (e.g., to define, identify, and/oranalyze one or more value subjects).

According to some embodiments, the risk assessment instructions 1542-2may be operable to cause the processing device 1512 to perform a riskassessment as described herein. Location data 1544-1 (e.g., certifiedlocation data and/or relationships) of an object and/or area and/orother location data 1544-1, value subject data 1544-2, customer data1544-3, and/or claim/loss data 1544-4 may be analyzed to create lossdistributions, for example, that may be utilized to generate a riskscore for an object and/or area being insured (e.g., in accordance withthe method 1100 of FIG. 11). The risk assessment instructions 1542-2may, in some embodiments, utilize the location data 1544-1, valuesubject data 1544-2, customer data 1544-3, and/or claim/loss data 1544-4to determine relationships between objects/areas for which insurance issought and related objects/areas that are not the subject of aninsurance product under evaluation (e.g., the location data 1544-1 may,in addition to storing information on objects such as buildings that areinsured, store information relating such buildings to other buildings,roads, intersections, and/or other externality objects that may berelated to the building). According to some embodiments, the riskassessment instructions 1542-2 may be utilized to accept user-definedpoint, line, and/or polygon data (e.g., via the interface 1520)identifying and/or defining one or more value subjects for which dataand/or analysis are desired.

In some embodiments, the premium determination instructions 1542-3 maybe executed by the processing device 1512 to calculate an insurancepremium for an insurance product (e.g., based on the location data1544-1, value subject data 1544-2, customer data 1544-3, and/orclaim/loss data 1544-4). According to some embodiments, the riskassessment instructions 1542-2 and/or the premium determinationinstructions 1542-3 may utilize the location data 1544-1, value subjectdata 1544-2, customer data 1544-3, and/or claim/loss data 1544-4 (suchas by implementing the location-based risk/loss control method 1400 ofFIG. 14) to update and/or revise risk and/or premium determinations,respectively. The apparatus 1500 may function as a computer terminaland/or server of an insurance and/or underwriting company, for example,that is utilized to process insurance applications. In some embodiments,the apparatus 1500 may comprise a web server and/or other portal (e.g.,an IVRU) that provides location data 1544-1, value subject data 1544-2,customer data 1544-3, and/or claim/loss data 1544-4 to consumers, datacustomers, and/or corporations.

Any or all of the exemplary instructions and data types described hereinand other practicable types of data may be stored in any number, type,and/or configuration of memory devices that is or becomes known. Thememory device 1540 may, for example, comprise one or more data tables orfiles, databases, table spaces, registers, and/or other storagestructures. In some embodiments, multiple databases and/or storagestructures (and/or multiple memory devices 1540) may be utilized tostore information associated with the apparatus 1500. According to someembodiments, the memory device 1540 may be incorporated into and/orotherwise coupled to the apparatus 1500 (e.g., as shown) or may simplybe accessible to the apparatus 1500 (e.g., externally located and/orsituated).

In some embodiments, the apparatus 1500 may comprise a cooling device1550. According to some embodiments, the cooling device 1550 may becoupled (physically, thermally, and/or electrically) to the processingdevice 1512 and/or to the memory device 1540. The cooling device 1550may, for example, comprise a fan, heat sink, heat pipe, radiator, coldplate, and/or other cooling component or device or combinations thereof,configured to remove heat from portions or components of the apparatus1500.

Referring now to FIG. 16A, FIG. 16B, FIG. 16C, FIG. 16D, and FIG. 16E,perspective diagrams of exemplary data storage devices 1640 a-eaccording to some embodiments are shown. The data storage devices 1640a-e may, for example, be utilized to store instructions and/or data suchas the certified location instructions 1542-1, risk assessmentinstructions 1542-2, premium determination instructions 1542-3, locationdata 1544-1, value subject data 1544-2, customer data 1544-3, and/orclaim/loss data 1544-4, each of which is described in reference to FIG.15 herein. In some embodiments, instructions stored on the data storagedevices 1640 a-e may, when executed by a processor (such as theelectronic processor 1512 of FIG. 15), cause the implementation ofand/or facilitate the methods 400, 700, 800, 1000, 1100, 1200, 1400described in conjunction with FIG. 4, FIG. 7, FIG. 8, FIG. 10, FIG. 11,FIG. 12, and/or FIG. 14, as described herein, and/or portions orcombinations thereof.

According to some embodiments, a first data storage device 1640 a maycomprise one or more various types of internal and/or external harddrives. The first data storage device 1640 a may, for example, comprisea data storage medium 1646 that is read, interrogated, and/or otherwisecommunicatively coupled to and/or via a disk reading device 1648. Insome embodiments, the first data storage device 1640 a and/or the datastorage medium 1646 may be configured to store information utilizing oneor more magnetic, inductive, and/or optical means (e.g., magnetic,inductive, and/or optical-encoding). The data storage medium 1646,depicted as a first data storage medium 1646 a for example (e.g.,breakout cross-section “A”), may comprise one or more of a polymer layer1646 a-1, a magnetic data storage layer 1646 a-2, a non-magnetic layer1646 a-3, a magnetic base layer 1646 a-4, a contact layer 1646 a-5,and/or a substrate layer 1646 a-6. According to some embodiments, amagnetic read head 1646 a may be coupled and/or disposed to read datafrom the magnetic data storage layer 1646 a-2.

In some embodiments, the data storage medium 1646, depicted as a seconddata storage medium 1646 b for example (e.g., breakout cross-section“B”), may comprise a plurality of data points 1646 b-2 disposed with thesecond data storage medium 1646 b. The data points 1646 b-2 may, in someembodiments, be read and/or otherwise interfaced with via alaser-enabled read head 1648 b disposed and/or coupled to direct a laserbeam through the second data storage medium 1646 b.

According to some embodiments, a second data storage device 1640 b maycomprise a CD, CD-ROM, DVD, Blu-Ray™ Disc, and/or other type ofoptically-encoded disk and/or other computer-readable storage mediumthat is or becomes know or practicable. In some embodiments, a thirddata storage device 1640 c may comprise a USB keyfob, dongle, and/orother type of flash memory data storage device that is or becomes knowor practicable. According to some embodiments, a fourth data storagedevice 1640 d may comprise RAM of any type, quantity, and/orconfiguration that is or becomes practicable and/or desirable. In someembodiments, the fourth data storage device 1640 d may comprise anoff-chip cache such as a Level 2 (L2) or Level 3 (L3) cache memorydevice. According to some embodiments, a fifth data storage device 1640e may comprise an on-chip memory device such as a Level 1 (L1) cachememory device.

The data storage devices 1640 a-e may generally store programinstructions, code, and/or modules that, when executed by an electronicand/or computerized processing device cause a particular machine tofunction in accordance with embodiments described herein. In someembodiments, the data storage devices 1640 a-e depicted in FIG. 16A,FIG. 16B, FIG. 16C, FIG. 16D, and FIG. 16E are representative of a classand/or subset of computer-readable media that are defined herein as“computer-readable memory” (e.g., memory devices as opposed totransmission devices). While computer-readable media may includetransitory media types, as utilized herein, the term computer-readablememory is limited to non-transitory computer-readable media.

The present disclosure provides, to one of ordinary skill in the art, anenabling description of several embodiments and/or inventions. Some ofthese embodiments and/or inventions may not be claimed in the presentapplication, but may nevertheless be claimed in one or more continuingapplications that claim the benefit of priority of the presentapplication. Applicants intend to file additional applications to pursuepatents for subject matter that has been disclosed and enabled but notclaimed in the present application.

What is claimed is:
 1. A non-transitory memory device storinginstructions that when executed by a processing device result in:receiving, via a graphical user interface comprising a graphicaldepiction of a geographical area, an indication of a user-definedlocation on the graphical depiction of the geographical area, whereinthe indication of the user-defined location comprises a user-definedpolygon received as drawing input via the graphical user interface;querying, utilizing the user-defined polygon, a database storing valuesubject data and location data related to the value subject data, thevalue subject data and location data related to the value subject databeing descriptive of a plurality of locations; identifying, based on thequerying of the database, a subset of the locations of the plurality oflocations that are bounded by the user-defined polygon of theuser-defined location, wherein the subset comprises more than onelocation; selecting, based on the identifying of the subset of thelocations of the plurality of locations that are bounded by theuser-defined polygon of the user-defined location, value subject datadescriptive of an attribute value for each of the locations of thesubset of the locations of the plurality of locations that are boundedby the user-defined polygon of the user-defined location; summing theselected data attribute values for the subset of the locations of theplurality of locations that are bounded by the user-defined polygon ofthe user-defined location; and providing, via the graphical userinterface, an indication of the summation of the selected attributevalues for the subset of the locations of the plurality of locationsthat are bounded by the user-defined polygon of the user-definedlocation.
 2. The non-transitory memory device of claim 1, wherein theidentifying of the subset of the locations of the plurality of locationsthat are bounded by the user-defined polygon of the user-definedlocation, comprises: identifying, based on the user-defined polygonindicative of the user-defined location, a plurality of coordinatesdescriptive of the user-defined location; identifying, based on thequerying of the database, and utilizing the plurality of coordinates, aplurality of possible value subjects; and selecting, from the pluralityof possible value subjects and based on a stored association with atleast one of the coordinates from the plurality of coordinates, thevalue subject from the plurality of possible value subjects.
 3. Thenon-transitory memory device of claim 1, wherein the indication of theuser-defined location comprises the user-defined polygon, and whereinthe querying comprises: identifying a plurality of points defining theuser-defined polygon indicative of the user-defined location; convertingdata descriptive of the plurality of points into a coordinate pointformat; and querying, utilizing the coordinate point formatted datadescriptive of the plurality of points, the database.
 4. Thenon-transitory memory device of claim 1, wherein the data attributevalues comprise values for a data attribute comprising total realizedloss.
 5. The non-transitory memory device of claim 1, wherein the dataattribute values comprise values for a data attribute comprising totalrisk.
 6. The non-transitory memory device of claim 1, wherein the dataattribute values comprise values for a data attribute comprising totalexposure.
 7. The non-transitory memory device of claim 1, wherein theinstructions, when executed by the processing device, further result in:providing the graphical user interface.
 8. The non-transitory memorydevice of claim 1, wherein the instructions, when executed by theprocessing device, further result in: receiving a fee in exchange forthe providing of the indication of the summation of the selectedattribute values for the subset of the locations of the plurality oflocations that are bounded by the user-defined polygon of theuser-defined location.
 9. A method for obtaining a summation ofattribute values for a plurality of locations that are bounded by auser-defined polygon, comprising: receiving, via a graphical userinterface comprising a graphical depiction of a geographical area, anindication of a user-defined location on the graphical depiction of thegeographical area, wherein the indication of the user-defined locationcomprises a user-defined polygon received as drawing input via thegraphical user interface; querying, utilizing the user-defined polygon,a database storing value subject data and location data related to thevalue subject data, the value subject data and location data related tothe value subject data being descriptive of a plurality of locations;identifying, based on the querying of the database, a subset of thelocations of the plurality of locations that are bounded by theuser-defined polygon of the user-defined location, wherein the subsetcomprises more than one location; selecting, based on the identifying ofthe subset of the locations of the plurality of locations that arebounded by the user-defined polygon of the user-defined location, valuesubject data descriptive of an attribute value for each of the locationsof the subset of the locations of the plurality of locations that arebounded by the user-defined polygon of the user-defined location;summing the selected data attribute values for the subset of thelocations of the plurality of locations that are bounded by theuser-defined polygon of the user-defined location; and providing, viathe graphical user interface, an indication of the summation of theselected attribute values for the subset of the locations of theplurality of locations that are bounded by the user-defined polygon ofthe user-defined location.
 10. The method of claim 9, wherein theidentifying of the subset of the locations of the plurality of locationsthat are bounded by the user-defined polygon of the user-definedlocation, comprises: identifying, based on the user-defined polygonindicative of the user-defined location, a plurality of coordinatesdescriptive of the user-defined location; identifying, based on thequerying of the database, and utilizing the plurality of coordinates, aplurality of possible value subjects; and selecting, from the pluralityof possible value subjects and based on a stored association with atleast one of the coordinates from the plurality of coordinates, thevalue subject from the plurality of possible value subjects.
 11. Themethod of claim 9, wherein the indication of the user-defined locationcomprises the user-defined polygon, and wherein the querying comprises:identifying a plurality of points defining the user-defined polygonindicative of the user-defined location; converting data descriptive ofthe plurality of points into a coordinate point format; and querying,utilizing the coordinate point formatted data descriptive of theplurality of points, the database.
 12. The method of claim 9, whereinthe data attribute values comprise values for a data attributecomprising total realized loss.
 13. The method of claim 9, wherein thedata attribute values comprise values for a data attribute comprisingtotal risk.
 14. The method of claim 9, wherein the data attribute valuescomprise values for a data attribute comprising total exposure.
 15. Themethod of claim 9, further comprising: providing the graphical userinterface.
 16. The method of claim 9, further comprising: receiving afee in exchange for the providing of the indication of the summation ofthe selected attribute values for the subset of the locations of theplurality of locations that are bounded by the user-defined polygon ofthe user-defined location.